----------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/javier/Desktop/Papers/1. Under Review/Subnational Decoupling/1. Perspectives Submission/Review Round_2/T
> EXT, TABLES AND FIGURES/0Replication Files/MRDLog.log
  log type:  text
 opened on:  22 Jun 2024, 19:06:18

. use "/Users/javier/Desktop/V-Dem 2023/V-Dem-CY-Full+Others-v13.dta"
(V-Dem CY-Full+Others)

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *
. *STANDARDIZE VARIABLES OF INTEREST
. *GEN POSITIVE VALUES
. gen v2elffelr_pos=v2elffelr+3.459
(9,461 missing values generated)

. gen v2elsnlsff_pos=v2elsnlsff+3.219
(4,143 missing values generated)

. gen v2elfrfair_pos=v2elfrfair+3.373
(12,057 missing values generated)

. *STD BETWEEN 0 & 1
. foreach var in v2elffelr_pos v2xel_frefair v2elsnlsff_pos v2elfrfair_pos {
  2.         qui sum `var'
  3.         gen `var'_standard= (`var' - `r(min)') / (`r(max)'-`r(min)')
  4. }
(9,461 missing values generated)
(122 missing values generated)
(4,143 missing values generated)
(12,057 missing values generated)

. *CHECK
. sum v2xel_frefair_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2xel_fre~rd |     27,433    .2791193    .3392966          0          1

. sum v2elffelr_pos_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2elffel~ard |     18,094    .5536238    .2421002          0          1

. sum v2elsnlsff_pos_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2elsnls~ard |     23,412    .6127363    .2320111          0          1

. sum v2elfrfair_pos_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2elfrfa~ard |     15,498    .5430471    .2378209          0          1

. *
. *IDENTIFY SUBNATIONAL ELECTIONS BY REGIME TYPE
. gen polity_demdummy=.
(27,555 missing values generated)

. replace polity_demdummy=1 if e_polity2>5
(15,366 real changes made)

. replace polity_demdummy=0 if polity_demdummy==. & e_polity2==-66| e_polity2==-88 
(5 real changes made)

. replace polity_demdummy=0 if e_polity2<6
(12,184 real changes made)

. *GENERARTE SHARE
. bys year: egen subnat_elect_POLITYnatDEM=mean(v2elffelrbin_ord) if polity_demdummy==1
(12,189 missing values generated)

. bys year: egen subnat_elect_POLITYnatAUTH=mean(v2elffelrbin_ord) if polity_demdummy==0
(15,366 missing values generated)

. *SHARE 3-YEAR MA
. xtset country_id year

Panel variable: country_id (unbalanced)
 Time variable: year, 1789 to 2022, but with gaps
         Delta: 1 unit

. gen subnat_elect_POLITYnatDEM_MA=(F1.subnat_elect_POLITYnatDEM+subnat_elect_POLITYnatDEM+L1.subnat_elect_POLITYnatDEM)/3
(13,094 missing values generated)

. xtset country_id year

Panel variable: country_id (unbalanced)
 Time variable: year, 1789 to 2022, but with gaps
         Delta: 1 unit

. gen subnat_elect_POLITYnatAUTH_MA=(F1.subnat_elect_POLITYnatAUTH+subnat_elect_POLITYnatAUTH+L1.subnat_elect_POLITYnatAUTH)
> /3
(15,902 missing values generated)

. **************************************************************************************************************************
> ************************
. **************************************************************************************************************************
> ************************
. **************************************************************************************************************************
> ************************
. *FIGURE 1: The Puzzling Territorial Dimension of Autocratization & Contemporary Regime Change
. *PANEL A
. tsline subnat_elect_POLITYnatDEM_MA   if year>1999, yaxis(1) lcol(dkgreen) || ///
> tsline subnat_elect_POLITYnatAUTH_MA  if year>1999, yaxis(2) lcol(purple)  || /// 
> , xtitle("Year", size(small)) ytitle("Share Dem. (3-year moving avg.)", size(small) axis(1)) ///
>  ytitle("Share Auth. (3-year moving avg.)", size(small) axis(2)) /// 
>  subtitle("Share of Countries w/Subnat. Elections by Nat. Regime Type ", size(small)) /// 
>  graphregion(margin(2 2 2 2)) plotregion(margin(0 0 0 0)) ysize(1) xsize(1) /// 
>  ylabel(,labsize(small) axis(1)) ylabel(,labsize(small) axis(2)) xlabel(,labsize(small)) /// 
>  leg(pos(5) ring(0) row(2) label(1 "Dem.w/Subnat. Elections") label(2 "Auth.w/Subnat. Elections") size(small)) /// 
>  scheme(lean1) yscale(titlegap(0) axis(1)) yscale(titlegap(0) axis(2))

. *
. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *
. *PANEL B
. bys  year: egen corr_year_natsubnat=corr(v2elffelr_pos_standard v2elfrfair_pos_standard)
(14,214 missing values generated)

. *MOVING AVERAGE CORRELATION FIGURE
. xtset country_id year 

Panel variable: country_id (unbalanced)
 Time variable: year, 1789 to 2022, but with gaps
         Delta: 1 unit

. gen corr_year_natsubnat_MA=(F1.corr_year_natsubnat+corr_year_natsubnat+L1.corr_year_natsubnat)/3
(15,109 missing values generated)

. *FIGURE 1. PANEL B Correlation FIGURE
. tsline corr_year_natsubnat_MA if year>1999 & year<2023, /// 
>  ysize(1) xsize(1) ylabel(,labsize(small) axis(1)) xlabel(,labsize(small)) /// 
>  scheme(lean1) yscale(titlegap(0)) yscale(titlegap(0)) xlabel(2000(4)2022) /// 
> graphregion(margin(2 2 2 2)) plotregion(margin(0 0 0 0)) xtitle("Year", size(small)) /// 
> ytitle("Correlation Coefficient (3-year Moving Average)", size(small)) /// 
> subtitle("Corr. Between Free & Fair Elections across Territorial Scales", size(small)) /// 
> text(.838 2019 "All Countries", size(vsmall)) 

. *
. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. **************************************************************************************************************************
> ************************
. *FIGURE 3: Multilevel Regime Decoupling 1990-2022
. *INDEITFY PERIOD OF INTEREST
. gen period=. 
(27,555 missing values generated)

. replace period=1 if year>1989 & year<2000
(1,747 real changes made)

. replace period=2 if year>1999 & year<2010 
(1,773 real changes made)

. replace period=3 if year>2009 
(2,326 real changes made)

. replace period=-99 if period==.
(21,709 real changes made)

. *CONFIRM YEARS
. bys period: sum year

----------------------------------------------------------------------------------------------------------------------------
-> period = -99

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |     21,709    1907.444    56.44879       1789       1989

----------------------------------------------------------------------------------------------------------------------------
-> period = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |      1,747     1994.52    2.871636       1990       1999

----------------------------------------------------------------------------------------------------------------------------
-> period = 2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |      1,773    2004.506     2.87446       2000       2009

----------------------------------------------------------------------------------------------------------------------------
-> period = 3

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |      2,326    2016.003    3.741197       2010       2022


. *ESTIMATE AVERAGE DEM SCORE PER PERIOD
. gen e_polity2mod=e_polity2
(10,399 missing values generated)

. replace e_polity2mod=. if e_polity2<-10
(5 real changes made, 5 to missing)

. bys country_id period: egen mean_polity_period=mean(e_polity2mod)
(2,693 missing values generated)

. *IDENTIFY DEMOCRATIC REGIMES PER PERIOD. 
. gen dem_period_dummy=. 
(27,555 missing values generated)

. replace dem_period_dummy=1 if mean_polity_period>5
(8,180 real changes made)

. replace dem_period_dummy=0 if dem_period_dummy==. 
(19,375 real changes made)

. *
. keep country_id year e_regionpol v2elffelr_pos_standard v2xel_frefair_standard v2elsnlsff_pos_standard v2elfrfair_pos_stan
> dard period dem_period_dummy 

. *
. keep if year>1989
(21,709 observations deleted)

. *
. bysort country_id period (year): gen dem_nat_ave = (v2elfrfair_pos_standard + v2elfrfair_pos_standard[_n-1])/2 if year == 
> year[_n-1] + 1
(1,061 missing values generated)

. bysort country_id period (year): gen dem_subnat_ave = (v2elffelr_pos_standard + v2elffelr_pos_standard[_n-1])/2 if year ==
>  year[_n-1] + 1
(1,205 missing values generated)

. *
. bysort country_id period: gen first_nat_dem=dem_nat_ave if _n==2
(5,387 missing values generated)

. bysort country_id period: gen second_nat_dem=dem_nat_ave if _n==_N
(5,360 missing values generated)

. *
. bysort country_id period: gen first_subnat_dem=dem_subnat_ave if _n==2
(5,389 missing values generated)

. bysort country_id period: gen second_subnat_dem=dem_subnat_ave if _n==_N
(5,376 missing values generated)

. *
. bysort country_id period: egen mean_first_nat_dem=mean(first_nat_dem)
(763 missing values generated)

. bysort country_id period: egen mean_second_nat_dem=mean(second_nat_dem)
(509 missing values generated)

. *
. bysort country_id period: egen mean_first_subnat_dem=mean(first_subnat_dem)
(820 missing values generated)

. bysort country_id period: egen mean_second_subnat_dem=mean(second_subnat_dem)
(692 missing values generated)

. *
. *GENERATE  DIFFERENCES
. gen nat_difference=mean_second_nat_dem-mean_first_nat_dem
(957 missing values generated)

. gen subnat_difference=mean_second_subnat_dem-mean_first_subnat_dem
(1,010 missing values generated)

. *
. *GEN CLASSIFICATION OF QUADRANTS
. gen class_difflevel=. 
(5,846 missing values generated)

. replace class_difflevel=1 if nat_difference>=0  & subnat_difference>=0
(3,178 real changes made)

. replace class_difflevel=2 if nat_difference<0  & subnat_difference>=0
(1,389 real changes made)

. replace class_difflevel=3 if nat_difference<0  & subnat_difference<0
(832 real changes made)

. replace class_difflevel=4 if nat_difference>0  & subnat_difference<0
(447 real changes made)

. *
. *TABULATE ALL AND DEMOCRACIES ONLY
. tab period class_difflevel, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |               class_difflevel
    period |         1          2          3          4 |     Total
-----------+--------------------------------------------+----------
         1 |     1,288        300        110         49 |     1,747 
           |     73.73      17.17       6.30       2.80 |    100.00 
-----------+--------------------------------------------+----------
         2 |     1,123        400        150        100 |     1,773 
           |     63.34      22.56       8.46       5.64 |    100.00 
-----------+--------------------------------------------+----------
         3 |       767        689        572        298 |     2,326 
           |     32.98      29.62      24.59      12.81 |    100.00 
-----------+--------------------------------------------+----------
     Total |     3,178      1,389        832        447 |     5,846 
           |     54.36      23.76      14.23       7.65 |    100.00 

. tab period class_difflevel if dem_period_dummy==1, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |               class_difflevel
    period |         1          2          3          4 |     Total
-----------+--------------------------------------------+----------
         1 |       610        190         40          9 |       849 
           |     71.85      22.38       4.71       1.06 |    100.00 
-----------+--------------------------------------------+----------
         2 |       713        180         90         30 |     1,013 
           |     70.38      17.77       8.88       2.96 |    100.00 
-----------+--------------------------------------------+----------
         3 |       364        559        312        143 |     1,378 
           |     26.42      40.57      22.64      10.38 |    100.00 
-----------+--------------------------------------------+----------
     Total |     1,687        929        442        182 |     3,240 
           |     52.07      28.67      13.64       5.62 |    100.00 

. *TABULATE BY REGION
. egen tag=tag(country_id period class_difflevel)

. keep if tag==1
(5,310 observations deleted)

. tab e_regionpol class_difflevel if period==1 & dem_period_dummy==1, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

               Region |               class_difflevel
(politico-geographic) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
E. Europe and C. Asia |        12          3          1          1 |        17 
                      |     70.59      17.65       5.88       5.88 |    100.00 
----------------------+--------------------------------------------+----------
        Latin America |        12          1          2          0 |        15 
                      |     80.00       6.67      13.33       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                 MENA |         3          1          0          0 |         4 
                      |     75.00      25.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
   Sub-Saharan Africa |         9          2          0          0 |        11 
                      |     81.82      18.18       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
W. Europe and N. Amer |        15          9          0          0 |        24 
                      |     62.50      37.50       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
            East Asia |         4          0          0          0 |         4 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
      South-East Asia |         2          0          1          0 |         3 
                      |     66.67       0.00      33.33       0.00 |    100.00 
----------------------+--------------------------------------------+----------
           South Asia |         2          2          0          0 |         4 
                      |     50.00      50.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
          The Pacific |         2          1          0          0 |         3 
                      |     66.67      33.33       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        The Carribean |         3          0          0          0 |         3 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                Total |        64         19          4          1 |        88 
                      |     72.73      21.59       4.55       1.14 |    100.00 

. tab e_regionpol class_difflevel if period==2 & dem_period_dummy==1, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

               Region |               class_difflevel
(politico-geographic) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
E. Europe and C. Asia |        14          4          3          0 |        21 
                      |     66.67      19.05      14.29       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        Latin America |        10          5          2          0 |        17 
                      |     58.82      29.41      11.76       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                 MENA |         3          1          1          0 |         5 
                      |     60.00      20.00      20.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
   Sub-Saharan Africa |        10          6          1          1 |        18 
                      |     55.56      33.33       5.56       5.56 |    100.00 
----------------------+--------------------------------------------+----------
W. Europe and N. Amer |        23          0          0          1 |        24 
                      |     95.83       0.00       0.00       4.17 |    100.00 
----------------------+--------------------------------------------+----------
            East Asia |         4          0          0          0 |         4 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
      South-East Asia |         1          1          1          1 |         4 
                      |     25.00      25.00      25.00      25.00 |    100.00 
----------------------+--------------------------------------------+----------
           South Asia |         1          1          1          0 |         3 
                      |     33.33      33.33      33.33       0.00 |    100.00 
----------------------+--------------------------------------------+----------
          The Pacific |         2          0          0          0 |         2 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        The Carribean |         4          0          0          0 |         4 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                Total |        72         18          9          3 |       102 
                      |     70.59      17.65       8.82       2.94 |    100.00 

. tab e_regionpol class_difflevel if period==3 & dem_period_dummy==1, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

               Region |               class_difflevel
(politico-geographic) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
E. Europe and C. Asia |         5          9          5          3 |        22 
                      |     22.73      40.91      22.73      13.64 |    100.00 
----------------------+--------------------------------------------+----------
        Latin America |         3          5          5          3 |        16 
                      |     18.75      31.25      31.25      18.75 |    100.00 
----------------------+--------------------------------------------+----------
                 MENA |         2          1          1          1 |         5 
                      |     40.00      20.00      20.00      20.00 |    100.00 
----------------------+--------------------------------------------+----------
   Sub-Saharan Africa |        10          4          6          1 |        21 
                      |     47.62      19.05      28.57       4.76 |    100.00 
----------------------+--------------------------------------------+----------
W. Europe and N. Amer |         2         18          2          2 |        24 
                      |      8.33      75.00       8.33       8.33 |    100.00 
----------------------+--------------------------------------------+----------
            East Asia |         0          3          0          1 |         4 
                      |      0.00      75.00       0.00      25.00 |    100.00 
----------------------+--------------------------------------------+----------
      South-East Asia |         2          0          2          0 |         4 
                      |     50.00       0.00      50.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
           South Asia |         1          0          3          0 |         4 
                      |     25.00       0.00      75.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
          The Pacific |         2          0          0          0 |         2 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        The Carribean |         1          3          0          0 |         4 
                      |     25.00      75.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                Total |        28         43         24         11 |       106 
                      |     26.42      40.57      22.64      10.38 |    100.00 

. *
. *PANEL A 1990-2000
. scatter subnat_difference nat_difference if period==1, yline(0, lpat(dash) lcol(gs7)) xline(0, lpat(dash) lcol(gs7)) jitte
> r(2)  graphregion(margin(2 2 2 2)) plotregion(margin(0 0 0 0)) yscale(titlegap(0)) ysize(1) xsize(1)  ylabel(-.6(.2).8) xl
> abel(-.6(.2).8) subtitle("1990-2000", pos(12)) xtitle("Nat. Free & Fair {&Delta} ") ytitle("Subnat. Free & Fair {&Delta} "
> ) msymbol(circle_hollow) mlw(vthin) mlcol(black)  msize(small) scheme(lean1) /// 
> text(.7 .7 "I" .7 -.5 "II" -.5 .7 "IV" -.5 -.5 "III") /// 
> text(.6 .7 "74%" .6 -.5 "17%" -.4 .7 "3%" -.4 -.5 "6%", size(small)) /// 
> text(-.4 .2345 "H{subscript:o}: {&beta}Q{subscript:n}==0; F=34.6{superscript:***}", size(small)) /// 
> text(-.5 .2693 "H{subscript:o}: {&beta}Q{subscript:n}=={&beta}Q{subscript:n}; F=17.58{superscript:***}", size(small)) 

. *
. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *PANEL B 2000-2010
. scatter subnat_difference nat_difference if period==2, yline(0, lpat(dash) lcol(gs7)) xline(0, lpat(dash) lcol(gs7)) jitte
> r(2)   graphregion(margin(2 2 2 2)) plotregion(margin(0 0 0 0)) yscale(titlegap(0)) ysize(1) xsize(1)  ylabel(-.6(.2).8) x
> label(-.6(.2).8) subtitle("2000-2010", pos(12)) xtitle("Nat. Free & Fair {&Delta} ") ytitle("Subnat. Free & Fair {&Delta} 
> ") msymbol(circle_hollow) mlw(vthin) mlcol(black) msize(small) scheme(lean1) /// 
> text(.7 .7 "I" .7 -.5 "II" -.5 .7 "IV" -.5 -.5 "III") /// 
> text(.6 .7 "63%" .6 -.5 "23%" -.4 .7 "6%" -.4 -.5 "8%", size(small)) /// 
> text(-.4 .2345 "H{subscript:o}: {&beta}Q{subscript:n}==0; F=62.6{superscript:***}", size(small)) /// 
> text(-.5 .2693 "H{subscript:o}: {&beta}Q{subscript:n}=={&beta}Q{subscript:n}; F=48.02{superscript:***}", size(small)) 

. *
. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *PANEL C 2010-2022
. scatter subnat_difference nat_difference if period==3, yline(0, lpat(dash) lcol(gs7)) xline(0, lpat(dash) lcol(gs7)) jitte
> r(3)  graphregion(margin(2 2 2 2)) plotregion(margin(0 0 0 0)) yscale(titlegap(0)) ysize(1) xsize(1)  ylabel(-.6(.2).8) xl
> abel(-.6(.2).8) subtitle("2010-2022", pos(12)) xtitle("Nat. Free & Fair {&Delta} ") ytitle("Subnat. Free & Fair {&Delta} "
> ) msymbol(circle_hollow) mlw(vthin) mlcol(black) msize(small) scheme(lean1) /// 
> text(.7 .7 "I" .7 -.5 "II" -.5 .7 "IV" -.5 -.5 "III") /// 
> text(.6 .7 "33%" .6 -.5 "30%" -.4 .7 "13%" -.4 -.5 "25%", size(small))  /// 
> text(-.45 .249 "H{subscript:o}: {&beta}Q{subscript:n}==0; F=51.7{superscript:***}", size(small)) /// 
> text(-.55 .279 "H{subscript:o}: {&beta}Q{subscript:n}=={&beta}Q{subscript:n}; F=46.7{superscript:***}", size(small)) 

. *
. 
end of do-file

. use "/Users/javier/Desktop/V-Dem 2023/V-Dem-CY-Full+Others-v13.dta"
no; dataset in memory has changed since last saved
r(4);

. use "/Users/javier/Desktop/V-Dem 2023/V-Dem-CY-Full+Others-v13.dta",clear
(V-Dem CY-Full+Others)

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *FIGURE 4: Further Quantitative Assessments of Multilevel Regime Decoupling
. *PANEL A
. *LOAD V-DEM DATA
. use "V-Dem-CY-Full+Others-v13.dta", clear
(V-Dem CY-Full+Others)

. *SET AS PANEL 
. xtset country_id year

Panel variable: country_id (unbalanced)
 Time variable: year, 1789 to 2022, but with gaps
         Delta: 1 unit

. * STANDARDIZE VARIABLES BETWEEN 0-1
. gen v2elffelr_pos=v2elffelr+3.459
(9,461 missing values generated)

. gen v2elfrfair_pos=v2elfrfair+3.373
(12,057 missing values generated)

. *
. foreach var in v2elffelr_pos  v2elfrfair_pos {
  2.         qui sum `var'
  3.         gen `var'_standard= (`var' - `r(min)') / (`r(max)'-`r(min)')
  4. }
(9,461 missing values generated)
(12,057 missing values generated)

. *
. sum  v2elffelr_pos_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2elffel~ard |     18,094    .5536238    .2421002          0          1

. sum  v2elfrfair_pos_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2elfrfa~ard |     15,498    .5430471    .2378209          0          1

. *SIMPLIFY DF
. keep country_name country_id COWcode country_text_id year v2elffelr_pos_standard  v2elfrfair_pos_standard e_regionpol e_gd
> ppc e_wb_pop

. keep if year>1979
(20,138 observations deleted)

. *GENERATE ABSOLUTE DIFFERENCE
. gen natsubnatdiff=v2elfrfair_pos_standard-v2elffelr_pos_standard
(1,521 missing values generated)

. gen natsubnatdiff_abs=abs(natsubnatdiff)
(1,521 missing values generated)

. sum natsubnatdiff_abs

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
natsubnatd~s |      5,896    .0905619    .0805439   .0000219   .7002785

. *
. *EXPLORE ABSOLTE DIFFERENCE
. *Global Mean 
. bys year: egen natsubnatdiff_abs_globalyearmean=mean(natsubnatdiff_abs)

. xtset country_id year

Panel variable: country_id (unbalanced)
 Time variable: year, 1980 to 2022
         Delta: 1 unit

. gen MA_nsdiff_abs_globalyearmean=(F1.natsubnatdiff_abs_globalyearmean+natsubnatdiff_abs_globalyearmean+L1.natsubnatdiff_ab
> s_globalyearmean)/3
(362 missing values generated)

. *Period Identifiers
. gen period=. 
(7,417 missing values generated)

. replace period=1 if year>1989 & year<2000
(1,747 real changes made)

. replace period=2 if year>1999 & year<2010
(1,773 real changes made)

. replace period=3 if year>2009 & year<2023
(2,326 real changes made)

. *
. tsline MA_nsdiff_abs_globalyearmean if year>1989, /// 
> scheme(lean1) xtitle("Year", size(medsmall)) xlabel(, labsize(medsmall)) ///
> ytitle("|{&Delta}| Nat.-Subnat. Free & Fair (3-year MA)", size(medsmall)) ylabel(,labsize(medsmall)) ///
> graphregion(margin(2 2 2 2)) plotregion(margin(0 0 0 0)) yscale(titlegap(0)) ///
> yscale(titlegap(0)) ysize(1) xsize(1) ///
> xline(2000, lpattern(shortdash) lcol(maroon) lwidth(thin)) /// 
> xline(2010, lpattern(shortdash) lcol(maroon) lwidth(thin)) ///
> text(0.11 1994.5 "Panel A", size(medsmall)) /// 
> text(.108 1997.5 "Coverage: Global V-Dem Data", size(vsmall))

. 
end of do-file

. use "/Users/javier/Desktop/Papers/1. Under Review/Subnational Decoupling/1. Perspectives Submission/Review Round_2/TEXT, T
> ABLES AND FIGURES/0Replication Files/MergedAltMeasures.dta", clear
(Written by R.              )

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. gen SUBNAT_FIDALGO_pos = SUBNAT_FIDALGO  + 1.596858
(166 missing values generated)

. *STANDARDIZE
. foreach var in NATVdem_polyarchy NAT_VanhanenDem SUBNAT_ISED SUBNAT_FIDALGO_pos {
  2.         qui sum `var'
  3.         gen `var'_stnd= (`var' - `r(min)') / (`r(max)'-`r(min)')
  4. }
(24 missing values generated)
(156 missing values generated)
(166 missing values generated)

. *SET AS PANEL
. xtset country_id year

Panel variable: country_id (unbalanced)
 Time variable: year, 1990 to 2022
         Delta: 1 unit

. *ISED vs Vanhanen COMPARISON
. gen natsubnatdiff_VISED=  NAT_VanhanenDem_stnd - SUBNAT_ISED_stnd
(180 missing values generated)

. gen natsubnatdiff_VISED_abs=abs(natsubnatdiff_VISED)
(180 missing values generated)

. bys year: egen natsubnatdiff_VISED_absGlobal=mean(natsubnatdiff_VISED_abs)
(24 missing values generated)

. tsline natsubnatdiff_VISED_absGlobal

. *FIDALGO VS POLYARCHY(EDI) COMPARISON
. gen natsubnatdiff_FID=  NATVdem_polyarchy_stnd - SUBNAT_FIDALGO_pos_stnd
(166 missing values generated)

. gen natsubnatdiff_FID_abs=abs(natsubnatdiff_FID)
(166 missing values generated)

. bys year: egen natsubnatdiff_FID_absglobal=mean(natsubnatdiff_FID_abs)
(48 missing values generated)

. tsline natsubnatdiff_FID_absglobal

. *MOVING AVERAGES
. xtset country_id year

Panel variable: country_id (unbalanced)
 Time variable: year, 1990 to 2022
         Delta: 1 unit

. gen MAnatsubnatdiff_VISED_absGlobal=(F1.natsubnatdiff_VISED_absGlobal+natsubnatdiff_VISED_absGlobal+L1.natsubnatdiff_VISED
> _absGlobal)/3
(60 missing values generated)

. xtset country_id year

Panel variable: country_id (unbalanced)
 Time variable: year, 1990 to 2022
         Delta: 1 unit

. gen MAnatsubnatdiff_FID_absglobal=(F1.natsubnatdiff_FID_absglobal+natsubnatdiff_FID_absglobal+L1.natsubnatdiff_FID_absglob
> al)/3
(84 missing values generated)

. *
. tsline MAnatsubnatdiff_VISED_absGlobal, yaxis(1) lpat(shortdash) lcol(emerald%20) || ///
> lowess  MAnatsubnatdiff_VISED_absGlobal year, yaxis(1) lpat(solid)  lcol(dkgreen%80)  || ///
> tsline MAnatsubnatdiff_FID_absglobal, yaxis(2) lpat(shortdash)  lcol(emidblue%50) || /// 
> lowess  MAnatsubnatdiff_FID_absglobal year, yaxis(2) lpat(solid)  lcol(dknavy%80) ///
> ,scheme(lean1) /// 
> ytitle("|{&Delta}| Subnat. ISED vs Nat. Vanhanen (3-year MA)", size(medsmall) axis(1)) ///
> ytitle("|{&Delta}| Subnat. SEDS vs V-Dem Polyarchy (3-year MA)", size(medsmall) axis(2)) ///
> xtitle("Year", size(medsmall)) xlab(,labsize(medsmall)) ///
> legend(order(2 "ISED-Vanhanen" 4 "SEDS - V-Dem Polyarchy") size(vsmall) pos(5) ring(0)) ///
> ylabel(, labsize(medsmall) axis(1)) ylabel(, labsize(medsmall)axis(2))  graphregion(margin(2 2 2 2)) /// 
> plotregion(margin(0 0 0 0)) ysize(1) xsize(1) /// 
> text(.198 1999 "Panel B: Alt. Measures", size(medsmall)) /// 
> text(.195 1993 "Coverage:", size(vsmall)) /// 
> text(.193 1996.4 "SEDS: Federal countries", size(vsmall)) ///
> text(.191 1996 "ISED: Americas & India", size(vsmall))

. 
end of do-file

. use "/Users/javier/Desktop/Papers/1. Under Review/Subnational Decoupling/1. Perspectives Submission/Review Round_2/TEXT, T
> ABLES AND FIGURES/0Replication Files/RegressionNatSubnatDifferences.dta", clear

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *
. xtset country_id year

Panel variable: country_id (strongly balanced)
 Time variable: year, 1990 to 2023
         Delta: 1 unit

. *
. *Econ Development (World Bank GDP pc USD PPP)
. xtreg natsubnatdiff_abs gdppc_log   i.year, fe robust cluster(country_id) 

Fixed-effects (within) regression               Number of obs     =      4,512
Group variable: country_id                      Number of groups  =        162

R-squared:                                      Obs per group:
     Within  = 0.0345                                         min =          1
     Between = 0.0169                                         avg =       27.9
     Overall = 0.0005                                         max =         33

                                                F(33, 161)        =       1.84
corr(u_i, Xb) = -0.2209                         Prob > F          =     0.0071

                           (Std. err. adjusted for 162 clusters in country_id)
------------------------------------------------------------------------------
             |               Robust
natsubnatd~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   gdppc_log |   .0064707   .0098752     0.66   0.513     -.013031    .0259723
             |
        year |
       1991  |  -.0119632   .0072446    -1.65   0.101    -.0262699    .0023435
       1992  |  -.0052038   .0104723    -0.50   0.620    -.0258846     .015477
       1993  |  -.0192246    .010701    -1.80   0.074     -.040357    .0019079
       1994  |  -.0290884   .0106273    -2.74   0.007    -.0500752   -.0081016
       1995  |  -.0334487   .0104376    -3.20   0.002     -.054061   -.0128364
       1996  |  -.0324272   .0107182    -3.03   0.003    -.0535935   -.0112609
       1997  |  -.0371865    .010485    -3.55   0.001    -.0578923   -.0164806
       1998  |  -.0356141   .0104968    -3.39   0.001    -.0563433   -.0148848
       1999  |  -.0297654    .010764    -2.77   0.006    -.0510223   -.0085085
       2000  |  -.0277967   .0113209    -2.46   0.015    -.0501532   -.0054401
       2001  |  -.0315278   .0115349    -2.73   0.007    -.0543071   -.0087485
       2002  |  -.0368364   .0114434    -3.22   0.002    -.0594349   -.0142379
       2003  |  -.0376246   .0117696    -3.20   0.002    -.0608672    -.014382
       2004  |  -.0376961   .0121349    -3.11   0.002    -.0616602   -.0137321
       2005  |  -.0402881   .0124678    -3.23   0.001    -.0649097   -.0156665
       2006  |  -.0446623   .0129065    -3.46   0.001    -.0701501   -.0191745
       2007  |  -.0494423    .013374    -3.70   0.000    -.0758534   -.0230313
       2008  |  -.0505705   .0138377    -3.65   0.000    -.0778973   -.0232437
       2009  |  -.0520463   .0135936    -3.83   0.000    -.0788911   -.0252014
       2010  |  -.0517398    .013996    -3.70   0.000    -.0793792   -.0241004
       2011  |  -.0501827   .0144653    -3.47   0.001    -.0787488   -.0216166
       2012  |  -.0535145   .0147302    -3.63   0.000    -.0826038   -.0244252
       2013  |  -.0486263   .0149939    -3.24   0.001    -.0782364   -.0190162
       2014  |  -.0464485   .0150878    -3.08   0.002     -.076244   -.0166531
       2015  |  -.0474839   .0153912    -3.09   0.002    -.0778786   -.0170892
       2016  |  -.0498425   .0157894    -3.16   0.002    -.0810236   -.0186614
       2017  |  -.0482018   .0161301    -2.99   0.003    -.0800555    -.016348
       2018  |   -.048316   .0162062    -2.98   0.003    -.0803201    -.016312
       2019  |  -.0360985   .0162874    -2.22   0.028    -.0682631   -.0039339
       2020  |  -.0364204   .0162597    -2.24   0.026    -.0685301   -.0043107
       2021  |  -.0380843   .0164574    -2.31   0.022    -.0705846    -.005584
       2022  |  -.0400842   .0172543    -2.32   0.021    -.0741582   -.0060102
             |
       _cons |   .0662749    .083283     0.80   0.427     -.098193    .2307428
-------------+----------------------------------------------------------------
     sigma_u |  .05293073
     sigma_e |  .05762245
         rho |  .45763772   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. outreg2 using mydoc.doc, replace keep(gdppc_log) ctitle(1) addtext(Country FE, Yes, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *Population (World Bank) 
. xtreg natsubnatdiff_abs e_wb_pop_log  i.year, fe robust cluster(country_id) 

Fixed-effects (within) regression               Number of obs     =      4,531
Group variable: country_id                      Number of groups  =        164

R-squared:                                      Obs per group:
     Within  = 0.0329                                         min =          1
     Between = 0.0048                                         avg =       27.6
     Overall = 0.0141                                         max =         32

                                                F(32, 163)        =       1.87
corr(u_i, Xb) = -0.1257                         Prob > F          =     0.0063

                           (Std. err. adjusted for 164 clusters in country_id)
------------------------------------------------------------------------------
             |               Robust
natsubnatd~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
e_wb_pop_log |  -.0070128   .0246577    -0.28   0.776    -.0557025    .0416769
             |
        year |
       1991  |  -.0120951    .006491    -1.86   0.064    -.0249124    .0007223
       1992  |  -.0069816   .0095487    -0.73   0.466    -.0258367    .0118736
       1993  |  -.0204434   .0097245    -2.10   0.037    -.0396457   -.0012411
       1994  |  -.0274905   .0097084    -2.83   0.005    -.0466608   -.0083201
       1995  |  -.0309071   .0094498    -3.27   0.001    -.0495669   -.0122472
       1996  |  -.0295575   .0096008    -3.08   0.002    -.0485155   -.0105994
       1997  |  -.0337176   .0093627    -3.60   0.000    -.0522054   -.0152298
       1998  |  -.0332826   .0094761    -3.51   0.001    -.0519943   -.0145708
       1999  |  -.0273993   .0095255    -2.88   0.005    -.0462086     -.00859
       2000  |  -.0242833   .0099585    -2.44   0.016    -.0439475    -.004619
       2001  |  -.0287727   .0097509    -2.95   0.004     -.048027   -.0095184
       2002  |  -.0336022   .0098258    -3.42   0.001    -.0530044      -.0142
       2003  |  -.0340364   .0104371    -3.26   0.001    -.0546458    -.013427
       2004  |  -.0334581   .0103973    -3.22   0.002    -.0539888   -.0129274
       2005  |  -.0352191   .0105377    -3.34   0.001     -.056027   -.0144111
       2006  |  -.0388586   .0103609    -3.75   0.000    -.0593174   -.0183998
       2007  |  -.0420017   .0103592    -4.05   0.000    -.0624572   -.0215462
       2008  |  -.0417376   .0105096    -3.97   0.000    -.0624901   -.0209852
       2009  |   -.043128   .0105983    -4.07   0.000    -.0640557   -.0222003
       2010  |  -.0432451   .0108752    -3.98   0.000    -.0647196   -.0217705
       2011  |  -.0415125   .0107897    -3.85   0.000    -.0628182   -.0202068
       2012  |  -.0442449   .0109709    -4.03   0.000    -.0659083   -.0225816
       2013  |  -.0397609   .0115565    -3.44   0.001    -.0625806   -.0169412
       2014  |  -.0366964    .011898    -3.08   0.002    -.0601905   -.0132022
       2015  |  -.0377537    .011762    -3.21   0.002    -.0609791   -.0145283
       2016  |  -.0390477   .0118625    -3.29   0.001    -.0624717   -.0156237
       2017  |  -.0355259   .0121355    -2.93   0.004    -.0594889   -.0115629
       2018  |  -.0363891   .0124812    -2.92   0.004    -.0610349   -.0117434
       2019  |  -.0235361   .0126492    -1.86   0.065    -.0485136    .0014414
       2020  |  -.0251624   .0131016    -1.92   0.057    -.0510331    .0007084
       2021  |  -.0259512   .0131169    -1.98   0.050     -.051852   -.0000503
             |
       _cons |   .2300966   .3931739     0.59   0.559    -.5462744    1.006468
-------------+----------------------------------------------------------------
     sigma_u |  .05172423
     sigma_e |  .05754636
         rho |  .44686906   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(e_wb_pop_log) ctitle(2) addtext(Country FE, Yes, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *Ethnic Fractionalization 
. xtreg natsubnatdiff_abs ethnic_frac i.region_id i.year, robust cluster(country_id) 

Random-effects GLS regression                   Number of obs     =      2,753
Group variable: country_id                      Number of groups  =        142

R-squared:                                      Obs per group:
     Within  = 0.0388                                         min =          1
     Between = 0.1999                                         avg =       19.4
     Overall = 0.1357                                         max =         22

                                                Wald chi2(31)     =    3033.36
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                        (Std. err. adjusted for 142 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
              ethnic_frac |   .0206073   .0277589     0.74   0.458    -.0337991    .0750137
                          |
                region_id |
               East Asia  |  -.0225849   .0184846    -1.22   0.222    -.0588139    .0136442
           Latin America  |  -.0059659   .0108541    -0.55   0.583    -.0272395    .0153077
                    MENA  |   .0200128   .0170909     1.17   0.242    -.0134848    .0535105
              South Asia  |   .0382103   .0278056     1.37   0.169    -.0162877    .0927083
         South-East Asia  |   .0230835   .0209119     1.10   0.270    -.0179031      .06407
      Sub-Saharan Africa  |   .0408956   .0189386     2.16   0.031     .0037767    .0780146
           The Carribean  |    .007298    .015467     0.47   0.637    -.0230168    .0376128
             The Pacific  |   .1508998   .0099343    15.19   0.000     .1314289    .1703707
W. Europe and N. America  |   -.006248    .011106    -0.56   0.574    -.0280153    .0155193
                          |
                     year |
                    1991  |  -.0116324   .0073063    -1.59   0.111    -.0259525    .0026876
                    1992  |  -.0066485   .0107519    -0.62   0.536    -.0277218    .0144247
                    1993  |  -.0193577   .0101173    -1.91   0.056    -.0391873    .0004719
                    1994  |  -.0280267    .010118    -2.77   0.006    -.0478577   -.0081957
                    1995  |  -.0326818   .0100553    -3.25   0.001    -.0523898   -.0129738
                    1996  |  -.0313966   .0102732    -3.06   0.002    -.0515317   -.0112615
                    1997  |  -.0359186   .0100354    -3.58   0.000    -.0555876   -.0162496
                    1998  |   -.035006   .0100601    -3.48   0.001    -.0547235   -.0152886
                    1999  |  -.0289123   .0100454    -2.88   0.004    -.0486009   -.0092236
                    2000  |  -.0252892   .0105993    -2.39   0.017    -.0460635    -.004515
                    2001  |  -.0291772    .010692    -2.73   0.006     -.050133   -.0082213
                    2002  |  -.0327501   .0105051    -3.12   0.002    -.0533397   -.0121604
                    2003  |  -.0329041   .0106781    -3.08   0.002    -.0538328   -.0119754
                    2004  |  -.0325441   .0108191    -3.01   0.003    -.0537492    -.011339
                    2005  |  -.0350678   .0110552    -3.17   0.002    -.0567356      -.0134
                    2006  |  -.0394243   .0108967    -3.62   0.000    -.0607814   -.0180673
                    2007  |  -.0410065   .0108526    -3.78   0.000    -.0622773   -.0197358
                    2008  |  -.0401976   .0111129    -3.62   0.000    -.0619784   -.0184168
                    2009  |  -.0419605   .0110536    -3.80   0.000    -.0636251   -.0202959
                    2010  |  -.0422802   .0113539    -3.72   0.000    -.0645333    -.020027
                    2011  |  -.0413722   .0114427    -3.62   0.000    -.0637995   -.0189448
                          |
                    _cons |   .0952235   .0161011     5.91   0.000      .063666     .126781
--------------------------+----------------------------------------------------------------
                  sigma_u |  .04641349
                  sigma_e |  .05449757
                      rho |  .42039985   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(ethnic_frac) ctitle(3) addtext(Country FE, Region FE, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *Terrain Rugged Nunn & Puga 2012
. xtreg natsubnatdiff_abs terrain_rugged  i.region_id i.year , robust cl(country_id)

Random-effects GLS regression                   Number of obs     =      4,668
Group variable: country_id                      Number of groups  =        163

R-squared:                                      Obs per group:
     Within  = 0.0317                                         min =          1
     Between = 0.1655                                         avg =       28.6
     Overall = 0.1017                                         max =         33

                                                Wald chi2(42)     =     110.23
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                        (Std. err. adjusted for 163 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
           terrain_rugged |   .0000187   .0000345     0.54   0.587    -.0000489    .0000864
                          |
                region_id |
               East Asia  |   .0034131    .030826     0.11   0.912    -.0570047    .0638309
           Latin America  |  -.0015376   .0089161    -0.17   0.863    -.0190129    .0159376
                    MENA  |    .026092    .013384     1.95   0.051    -.0001402    .0523242
              South Asia  |   .0268146   .0166483     1.61   0.107    -.0058156    .0594448
         South-East Asia  |   .0174063   .0151602     1.15   0.251     -.012307    .0471197
      Sub-Saharan Africa  |   .0440202    .012196     3.61   0.000     .0201165    .0679238
           The Carribean  |  -.0112719   .0128403    -0.88   0.380    -.0364385    .0138947
             The Pacific  |    .037973   .0299143     1.27   0.204     -.020658     .096604
W. Europe and N. America  |   -.010751   .0086597    -1.24   0.214    -.0277236    .0062216
                          |
                     year |
                    1991  |  -.0125731   .0064232    -1.96   0.050    -.0251624    .0000161
                    1992  |  -.0076673   .0094111    -0.81   0.415    -.0261128    .0107781
                    1993  |   -.021102   .0096027    -2.20   0.028     -.039923   -.0022811
                    1994  |  -.0279931   .0096113    -2.91   0.004    -.0468309   -.0091552
                    1995  |  -.0313836   .0093521    -3.36   0.001    -.0497134   -.0130539
                    1996  |  -.0303367   .0095989    -3.16   0.002    -.0491501   -.0115233
                    1997  |  -.0345542   .0092986    -3.72   0.000    -.0527791   -.0163294
                    1998  |  -.0342028   .0093121    -3.67   0.000    -.0524542   -.0159515
                    1999  |  -.0282127   .0094204    -2.99   0.003    -.0466763   -.0097491
                    2000  |  -.0258018   .0099161    -2.60   0.009     -.045237   -.0063665
                    2001  |  -.0301225   .0099322    -3.03   0.002    -.0495892   -.0106558
                    2002  |  -.0347508   .0098354    -3.53   0.000    -.0540279   -.0154736
                    2003  |  -.0352476   .0101127    -3.49   0.000    -.0550681   -.0154271
                    2004  |  -.0349907   .0102772    -3.40   0.001    -.0551336   -.0148478
                    2005  |  -.0367583   .0104817    -3.51   0.000    -.0573021   -.0162145
                    2006  |  -.0405669   .0103884    -3.91   0.000    -.0609277    -.020206
                    2007  |  -.0439646   .0103865    -4.23   0.000    -.0643218   -.0236074
                    2008  |  -.0437714    .010484    -4.18   0.000    -.0643197   -.0232231
                    2009  |  -.0452359   .0103099    -4.39   0.000     -.065443   -.0250288
                    2010  |  -.0454955   .0105193    -4.32   0.000    -.0661129   -.0248781
                    2011  |  -.0436379   .0105481    -4.14   0.000    -.0643119   -.0229639
                    2012  |  -.0465784   .0105645    -4.41   0.000    -.0672844   -.0258724
                    2013  |  -.0424481   .0107378    -3.95   0.000    -.0634939   -.0214024
                    2014  |  -.0397839   .0108229    -3.68   0.000    -.0609964   -.0185713
                    2015  |  -.0405332    .010881    -3.73   0.000    -.0618595   -.0192068
                    2016  |  -.0423974   .0108677    -3.90   0.000    -.0636976   -.0210971
                    2017  |  -.0391937   .0110202    -3.56   0.000    -.0607929   -.0175946
                    2018  |  -.0398305   .0111514    -3.57   0.000    -.0616868   -.0179742
                    2019  |  -.0269756   .0112965    -2.39   0.017    -.0491163   -.0048349
                    2020  |  -.0288035   .0112007    -2.57   0.010    -.0507565   -.0068505
                    2021  |  -.0293075   .0110623    -2.65   0.008    -.0509893   -.0076257
                    2022  |  -.0308866   .0111497    -2.77   0.006    -.0527395   -.0090336
                          |
                    _cons |    .103611   .0115486     8.97   0.000     .0809762    .1262459
--------------------------+----------------------------------------------------------------
                  sigma_u |  .04268855
                  sigma_e |  .05776961
                      rho |  .35318593   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(terrain_rugged) ctitle(4) addtext(Country FE, Region FE, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *FEDERALISM Forum of Federations and Blume & Voigt
. xtreg natsubnatdiff_abs i.FEDERALISM_DUMMY  i.region_id i.year , robust cl(country_id)

Random-effects GLS regression                   Number of obs     =      4,715
Group variable: country_id                      Number of groups  =        165

R-squared:                                      Obs per group:
     Within  = 0.0319                                         min =          1
     Between = 0.1707                                         avg =       28.6
     Overall = 0.1006                                         max =         33

                                                Wald chi2(42)     =     105.41
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                        (Std. err. adjusted for 165 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
       1.FEDERALISM_DUMMY |   .0131122   .0087455     1.50   0.134    -.0040287    .0302531
                          |
                region_id |
               East Asia  |   .0048728   .0307885     0.16   0.874    -.0554716    .0652171
           Latin America  |  -.0046259   .0091479    -0.51   0.613    -.0225553    .0133036
                    MENA  |    .025003   .0131094     1.91   0.056    -.0006909    .0506969
              South Asia  |    .023098    .015135     1.53   0.127    -.0065661    .0527622
         South-East Asia  |   .0159421   .0137693     1.16   0.247    -.0110453    .0429294
      Sub-Saharan Africa  |   .0421512   .0116109     3.63   0.000     .0193943     .064908
           The Carribean  |  -.0127509   .0125146    -1.02   0.308    -.0372791    .0117774
             The Pacific  |    .038072   .0298216     1.28   0.202    -.0203772    .0965212
W. Europe and N. America  |  -.0147789   .0088007    -1.68   0.093    -.0320281    .0024702
                          |
                     year |
                    1991  |  -.0125701   .0064228    -1.96   0.050    -.0251586    .0000185
                    1992  |  -.0076787   .0094121    -0.82   0.415    -.0261261    .0107687
                    1993  |   -.021116   .0096035    -2.20   0.028    -.0399386   -.0022935
                    1994  |  -.0279966   .0096109    -2.91   0.004    -.0468337   -.0091595
                    1995  |  -.0313869   .0093517    -3.36   0.001    -.0497158   -.0130579
                    1996  |  -.0303453   .0095984    -3.16   0.002    -.0491578   -.0115327
                    1997  |  -.0345628   .0092979    -3.72   0.000    -.0527864   -.0163393
                    1998  |  -.0343786   .0093058    -3.69   0.000    -.0526176   -.0161395
                    1999  |  -.0284307    .009413    -3.02   0.003    -.0468799   -.0099815
                    2000  |  -.0260369   .0099044    -2.63   0.009    -.0454493   -.0066246
                    2001  |  -.0301304   .0099089    -3.04   0.002    -.0495514   -.0107093
                    2002  |  -.0341975   .0098419    -3.47   0.001    -.0534874   -.0149077
                    2003  |  -.0346916    .010111    -3.43   0.001    -.0545087   -.0148745
                    2004  |  -.0353869   .0102569    -3.45   0.001    -.0554901   -.0152837
                    2005  |  -.0372564   .0104645    -3.56   0.000    -.0577665   -.0167464
                    2006  |  -.0410223   .0103752    -3.95   0.000    -.0613573   -.0206874
                    2007  |  -.0443623   .0103727    -4.28   0.000    -.0646925   -.0240321
                    2008  |  -.0442477   .0104692    -4.23   0.000    -.0647669   -.0237284
                    2009  |  -.0456783   .0102956    -4.44   0.000    -.0658573   -.0254994
                    2010  |  -.0451122   .0105084    -4.29   0.000    -.0657083   -.0245161
                    2011  |  -.0434583   .0105287    -4.13   0.000    -.0640941   -.0228225
                    2012  |  -.0463709   .0105458    -4.40   0.000    -.0670402   -.0257015
                    2013  |  -.0420177   .0107191    -3.92   0.000    -.0630267   -.0210087
                    2014  |  -.0401519   .0108108    -3.71   0.000    -.0613406   -.0189632
                    2015  |  -.0408921   .0108667    -3.76   0.000    -.0621904   -.0195939
                    2016  |  -.0423655   .0108639    -3.90   0.000    -.0636584   -.0210727
                    2017  |  -.0389595   .0110171    -3.54   0.000    -.0605527   -.0173664
                    2018  |  -.0394168   .0111217    -3.54   0.000    -.0612149   -.0176187
                    2019  |  -.0265163   .0112588    -2.36   0.019    -.0485832   -.0044494
                    2020  |  -.0282875   .0111645    -2.53   0.011    -.0501696   -.0064054
                    2021  |  -.0288431   .0110267    -2.62   0.009    -.0504551   -.0072312
                    2022  |  -.0306968    .011114    -2.76   0.006    -.0524799   -.0089137
                          |
                    _cons |   .1064775    .010852     9.81   0.000      .085208    .1277469
--------------------------+----------------------------------------------------------------
                  sigma_u |  .04326313
                  sigma_e |  .05767021
                      rho |  .36011159   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(i.FEDERALISM_DUMMY) ctitle(5) addtext(Country FE, Region FE, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *STATE CAPACITY
. xtreg natsubnatdiff_abs inv_state_fsi i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,354
Group variable: country_id                      Number of groups  =        161

R-squared:                                      Obs per group:
     Within  = 0.0255                                         min =          1
     Between = 0.0631                                         avg =       14.6
     Overall = 0.0564                                         max =         17

                                                F(17, 160)        =       0.90
corr(u_i, Xb) = -0.1060                         Prob > F          =     0.5715

                            (Std. err. adjusted for 161 clusters in country_id)
-------------------------------------------------------------------------------
              |               Robust
natsubnatdi~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
inv_state_fsi |  -.0008861   .0006018    -1.47   0.143    -.0020745    .0003023
              |
         year |
        2007  |  -.0035202   .0039376    -0.89   0.373    -.0112966    .0042562
        2008  |  -.0024173   .0047363    -0.51   0.610     -.011771    .0069365
        2009  |  -.0048729   .0053454    -0.91   0.363    -.0154295    .0056837
        2010  |  -.0059032   .0062297    -0.95   0.345    -.0182063    .0063998
        2011  |  -.0020923    .006368    -0.33   0.743    -.0146685    .0104838
        2012  |   -.004624   .0063803    -0.72   0.470    -.0172245    .0079766
        2013  |   .0002449   .0061382     0.04   0.968    -.0118773    .0123672
        2014  |   .0029084     .00602     0.48   0.630    -.0089806    .0147974
        2015  |   .0024178   .0060542     0.40   0.690    -.0095387    .0143743
        2016  |   .0007466   .0061843     0.12   0.904    -.0114668      .01296
        2017  |    .004914   .0063129     0.78   0.437    -.0075533    .0173813
        2018  |   .0049309   .0067803     0.73   0.468    -.0084595    .0183213
        2019  |   .0187652   .0084873     2.21   0.028     .0020035    .0355269
        2020  |   .0200543   .0086724     2.31   0.022     .0029272    .0371814
        2021  |   .0169386   .0082922     2.04   0.043     .0005623    .0333149
        2022  |   .0157466   .0084325     1.87   0.064    -.0009067       .0324
              |
        _cons |   .1309505   .0358801     3.65   0.000     .0600908    .2018102
--------------+----------------------------------------------------------------
      sigma_u |  .05487685
      sigma_e |  .04798119
          rho |  .56674057   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(inv_state_fsi) ctitle(6) addtext(Country FE, Yes, Year FE, Yes) label
mydoc.doc
dir : seeout

. *Measured with SFI
. xtreg natsubnatdiff_abs inv_state_sfi  i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      3,344
Group variable: country_id                      Number of groups  =        156

R-squared:                                      Obs per group:
     Within  = 0.0095                                         min =          1
     Between = 0.0190                                         avg =       21.4
     Overall = 0.0000                                         max =         24

                                                F(24, 155)        =       0.93
corr(u_i, Xb) = -0.1016                         Prob > F          =     0.5600

                            (Std. err. adjusted for 156 clusters in country_id)
-------------------------------------------------------------------------------
              |               Robust
natsubnatdi~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
inv_state_sfi |   .0003153   .0014781     0.21   0.831    -.0026045    .0032352
              |
         year |
        1996  |    .000997   .0028641     0.35   0.728    -.0046607    .0066547
        1997  |  -.0033499   .0034108    -0.98   0.328    -.0100876    .0033879
        1998  |  -.0025662   .0040979    -0.63   0.532    -.0106611    .0055287
        1999  |   .0033633   .0044869     0.75   0.455    -.0055001    .0122267
        2000  |   .0063925   .0055972     1.14   0.255    -.0046642    .0174493
        2001  |   .0025054   .0056958     0.44   0.661    -.0087461    .0137569
        2002  |   -.002367   .0059506    -0.40   0.691    -.0141218    .0093878
        2003  |  -.0029317   .0061869    -0.47   0.636    -.0151531    .0092898
        2004  |  -.0026259   .0066881    -0.39   0.695    -.0158376    .0105858
        2005  |  -.0043585   .0073021    -0.60   0.551     -.018783    .0100661
        2006  |  -.0086898   .0071441    -1.22   0.226    -.0228021    .0054225
        2007  |  -.0117147   .0071579    -1.64   0.104    -.0258543    .0024249
        2008  |  -.0105276   .0071347    -1.48   0.142    -.0246213    .0035661
        2009  |  -.0119682    .007348    -1.63   0.105    -.0264834     .002547
        2010  |  -.0119396   .0075399    -1.58   0.115    -.0268338    .0029545
        2011  |  -.0099161   .0076668    -1.29   0.198    -.0250611    .0052288
        2012  |  -.0129518   .0077511    -1.67   0.097    -.0282633    .0023597
        2013  |  -.0089314   .0073201    -1.22   0.224    -.0233914    .0055287
        2014  |  -.0063212   .0074847    -0.84   0.400    -.0211064     .008464
        2015  |  -.0068976   .0076693    -0.90   0.370    -.0220474    .0082523
        2016  |  -.0087846   .0082051    -1.07   0.286    -.0249929    .0074237
        2017  |  -.0054886   .0087289    -0.63   0.530    -.0227316    .0117545
        2018  |  -.0055038   .0081804    -0.67   0.502    -.0216632    .0106557
              |
        _cons |    .080379   .0236406     3.40   0.001     .0336796    .1270784
--------------+----------------------------------------------------------------
      sigma_u |   .0522626
      sigma_e |  .05084421
          rho |  .51375395   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(inv_state_sfi) ctitle(7) addtext(Country FE, Yes, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *RAI 
. xtreg natsubnatdiff_abs rai_index  i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0361                                         min =         10
     Between = 0.0051                                         avg =       26.9
     Overall = 0.0237                                         max =         29

                                                F(29, 86)         =       2.12
corr(u_i, Xb) = -0.0188                         Prob > F          =     0.0040

                            (Std. err. adjusted for 87 clusters in country_id)
------------------------------------------------------------------------------
             |               Robust
natsubnatd~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   rai_index |   .0004067   .0010084     0.40   0.688    -.0015979    .0024114
             |
        year |
       1991  |  -.0133711   .0117389    -1.14   0.258    -.0367073     .009965
       1992  |   -.013453   .0113776    -1.18   0.240    -.0360709    .0091649
       1993  |   -.015138   .0111087    -1.36   0.177    -.0372213    .0069453
       1994  |  -.0195075    .011365    -1.72   0.090    -.0421004    .0030854
       1995  |  -.0183864   .0115466    -1.59   0.115    -.0413404    .0045675
       1996  |  -.0158289    .011708    -1.35   0.180    -.0391036    .0074457
       1997  |  -.0224036    .011442    -1.96   0.053    -.0451496    .0003424
       1998  |  -.0195515    .011388    -1.72   0.090    -.0421901    .0030871
       1999  |   -.013501   .0113363    -1.19   0.237    -.0360368    .0090348
       2000  |  -.0087361   .0128598    -0.68   0.499    -.0343004    .0168283
       2001  |  -.0151924   .0122015    -1.25   0.216    -.0394481    .0090633
       2002  |  -.0200076   .0117079    -1.71   0.091    -.0432821     .003267
       2003  |  -.0213451   .0120706    -1.77   0.081    -.0453406    .0026505
       2004  |  -.0137439   .0124522    -1.10   0.273     -.038498    .0110103
       2005  |  -.0146822   .0129519    -1.13   0.260    -.0404297    .0110653
       2006  |  -.0210962   .0124631    -1.69   0.094     -.045872    .0036796
       2007  |  -.0322819   .0120902    -2.67   0.009    -.0563165   -.0082473
       2008  |  -.0360227   .0122387    -2.94   0.004    -.0603525   -.0116929
       2009  |  -.0408783   .0121393    -3.37   0.001    -.0650104   -.0167462
       2010  |  -.0382988   .0122211    -3.13   0.002    -.0625935    -.014004
       2011  |  -.0347375   .0124975    -2.78   0.007    -.0595817   -.0098934
       2012  |  -.0335697    .012618    -2.66   0.009    -.0586534    -.008486
       2013  |  -.0271922    .012832    -2.12   0.037    -.0527015    -.001683
       2014  |  -.0249622   .0132668    -1.88   0.063    -.0513357    .0014113
       2015  |  -.0286866   .0130531    -2.20   0.031    -.0546353   -.0027379
       2016  |  -.0290693   .0134058    -2.17   0.033    -.0557191   -.0024195
       2017  |  -.0267428    .014053    -1.90   0.060    -.0546793    .0011937
       2018  |  -.0225697   .0146063    -1.55   0.126    -.0516062    .0064667
             |
       _cons |   .0903211   .0131892     6.85   0.000     .0641019    .1165404
-------------+----------------------------------------------------------------
     sigma_u |  .03792709
     sigma_e |  .04785176
         rho |  .38582772   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(rai_index) ctitle(8) addtext(Country FE, Yes, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *EMB CAPACITY Garnett
. xtreg natsubnatdiff_abs emb_overall_capacity  i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =      2,792
Group variable: country_id                      Number of groups  =         94

R-squared:                                      Obs per group:
     Within  = 0.0333                                         min =          1
     Between = 0.1677                                         avg =       29.7
     Overall = 0.0965                                         max =         33

                                                Wald chi2(41)     =     118.39
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                         (Std. err. adjusted for 94 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
     emb_overall_capacity |    .004471   .0069933     0.64   0.523    -.0092357    .0181777
                          |
                region_id |
               East Asia  |  -.0227733    .010287    -2.21   0.027    -.0429355    -.002611
           Latin America  |  -.0120778    .010957    -1.10   0.270    -.0335531    .0093975
                    MENA  |   .0108948    .016504     0.66   0.509    -.0214525    .0432421
              South Asia  |   .0211476   .0197794     1.07   0.285    -.0176194    .0599146
         South-East Asia  |   .0291427   .0200758     1.45   0.147    -.0102052    .0684906
      Sub-Saharan Africa  |   .0440384   .0175147     2.51   0.012     .0097103    .0783665
             The Pacific  |   .0507638   .0547168     0.93   0.354    -.0564792    .1580067
W. Europe and N. America  |  -.0069783   .0110247    -0.63   0.527    -.0285864    .0146298
                          |
                     year |
                    1991  |  -.0153768    .009742    -1.58   0.114    -.0344707    .0037172
                    1992  |  -.0113945   .0132606    -0.86   0.390    -.0373848    .0145958
                    1993  |  -.0198229   .0123182    -1.61   0.108    -.0439661    .0043203
                    1994  |  -.0270835   .0131118    -2.07   0.039    -.0527822   -.0013847
                    1995  |  -.0344276   .0127912    -2.69   0.007     -.059498   -.0093573
                    1996  |  -.0341793   .0129252    -2.64   0.008    -.0595123   -.0088463
                    1997  |  -.0374346   .0129602    -2.89   0.004    -.0628361   -.0120331
                    1998  |  -.0355531   .0127936    -2.78   0.005    -.0606281   -.0104781
                    1999  |  -.0279051   .0127277    -2.19   0.028    -.0528509   -.0029593
                    2000  |  -.0277057   .0132875    -2.09   0.037    -.0537486   -.0016628
                    2001  |  -.0309377   .0134094    -2.31   0.021    -.0572197   -.0046558
                    2002  |   -.037495   .0130404    -2.88   0.004    -.0630538   -.0119362
                    2003  |   -.035013   .0135376    -2.59   0.010    -.0615463   -.0084797
                    2004  |  -.0352053   .0138941    -2.53   0.011    -.0624371   -.0079734
                    2005  |   -.035632   .0142414    -2.50   0.012    -.0635447   -.0077194
                    2006  |  -.0462253   .0138398    -3.34   0.001    -.0733509   -.0190998
                    2007  |  -.0456264   .0137436    -3.32   0.001    -.0725635   -.0186894
                    2008  |  -.0474391   .0138921    -3.41   0.001    -.0746671    -.020211
                    2009  |  -.0507721   .0136478    -3.72   0.000    -.0775213   -.0240228
                    2010  |   -.048702   .0143045    -3.40   0.001    -.0767383   -.0206657
                    2011  |  -.0486342   .0144448    -3.37   0.001    -.0769454   -.0203229
                    2012  |  -.0506155   .0145307    -3.48   0.000    -.0790952   -.0221359
                    2013  |  -.0450133   .0144765    -3.11   0.002    -.0733868   -.0166399
                    2014  |  -.0433942   .0144504    -3.00   0.003    -.0717165   -.0150719
                    2015  |  -.0416622   .0146923    -2.84   0.005    -.0704585   -.0128658
                    2016  |  -.0415676   .0151547    -2.74   0.006    -.0712702    -.011865
                    2017  |  -.0374062   .0155068    -2.41   0.016     -.067799   -.0070134
                    2018  |  -.0393407   .0154134    -2.55   0.011    -.0695504    -.009131
                    2019  |  -.0312077   .0155463    -2.01   0.045    -.0616779   -.0007376
                    2020  |  -.0312592   .0159409    -1.96   0.050    -.0625027   -.0000157
                    2021  |  -.0349898   .0152809    -2.29   0.022    -.0649399   -.0050397
                    2022  |  -.0349721   .0155979    -2.24   0.025    -.0655434   -.0044009
                          |
                    _cons |   .1051397   .0192779     5.45   0.000     .0673558    .1429236
--------------------------+----------------------------------------------------------------
                  sigma_u |  .04229735
                  sigma_e |  .05939583
                      rho |  .33648472   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(emb_overall_capacity) ctitle(9) addtext(Country FE, Region FE, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *EMB Type IDEA 
. xtreg natsubnatdiff_abs i.emb_typeBIN  i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =      4,706
Group variable: country_id                      Number of groups  =        164

R-squared:                                      Obs per group:
     Within  = 0.0318                                         min =          1
     Between = 0.2026                                         avg =       28.7
     Overall = 0.1192                                         max =         33

                                                Wald chi2(42)     =     114.56
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                        (Std. err. adjusted for 164 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
              emb_typeBIN |
        Government/Mixed  |  -.0234075   .0085351    -2.74   0.006     -.040136   -.0066791
                          |
                region_id |
               East Asia  |  -.0146378   .0134312    -1.09   0.276    -.0409624    .0116868
           Latin America  |  -.0040441   .0086783    -0.47   0.641    -.0210532     .012965
                    MENA  |    .029876   .0130432     2.29   0.022     .0043117    .0554403
              South Asia  |   .0249021   .0168614     1.48   0.140    -.0081457    .0579498
         South-East Asia  |    .019425   .0135006     1.44   0.150    -.0070357    .0458857
      Sub-Saharan Africa  |   .0453943   .0116168     3.91   0.000     .0226257    .0681629
           The Carribean  |  -.0159777    .012473    -1.28   0.200    -.0404243    .0084689
             The Pacific  |   .0407259   .0297323     1.37   0.171    -.0175485    .0990002
W. Europe and N. America  |   .0043617   .0099155     0.44   0.660    -.0150724    .0237957
                          |
                     year |
                    1991  |  -.0125469   .0064256    -1.95   0.051    -.0251408    .0000471
                    1992  |  -.0076658   .0094106    -0.81   0.415    -.0261103    .0107788
                    1993  |  -.0210925   .0096019    -2.20   0.028    -.0399119   -.0022732
                    1994  |  -.0280459   .0096221    -2.91   0.004    -.0469049   -.0091868
                    1995  |  -.0314478   .0093608    -3.36   0.001    -.0497947   -.0131009
                    1996  |  -.0303046   .0095972    -3.16   0.002    -.0491147   -.0114944
                    1997  |  -.0345323   .0092966    -3.71   0.000    -.0527533   -.0163114
                    1998  |  -.0343591   .0093042    -3.69   0.000     -.052595   -.0161231
                    1999  |  -.0284642   .0094233    -3.02   0.003    -.0469336   -.0099948
                    2000  |  -.0260225   .0099043    -2.63   0.009    -.0454346   -.0066104
                    2001  |  -.0301092   .0099084    -3.04   0.002    -.0495293   -.0106892
                    2002  |  -.0341384    .009843    -3.47   0.001    -.0534303   -.0148466
                    2003  |  -.0347263   .0101284    -3.43   0.001    -.0545777    -.014875
                    2004  |   -.035347   .0102565    -3.45   0.001    -.0554494   -.0152446
                    2005  |  -.0372329   .0104647    -3.56   0.000    -.0577432   -.0167225
                    2006  |   -.040993   .0103736    -3.95   0.000     -.061325    -.020661
                    2007  |  -.0445161   .0103873    -4.29   0.000    -.0648749   -.0241573
                    2008  |  -.0442177   .0104686    -4.22   0.000    -.0647358   -.0236995
                    2009  |  -.0456555   .0102958    -4.43   0.000    -.0658349    -.025476
                    2010  |  -.0450834   .0105097    -4.29   0.000    -.0656819   -.0244849
                    2011  |  -.0433002   .0105463    -4.11   0.000    -.0639704   -.0226299
                    2012  |  -.0463606   .0105472    -4.40   0.000    -.0670327   -.0256885
                    2013  |  -.0420229   .0107204    -3.92   0.000    -.0630346   -.0210112
                    2014  |  -.0401841   .0108138    -3.72   0.000    -.0613787   -.0189896
                    2015  |  -.0407472    .010884    -3.74   0.000    -.0620794   -.0194149
                    2016  |  -.0423493   .0108646    -3.90   0.000    -.0636435    -.021055
                    2017  |  -.0389791   .0110171    -3.54   0.000    -.0605723   -.0173859
                    2018  |  -.0394131   .0111229    -3.54   0.000    -.0612136   -.0176126
                    2019  |  -.0274664   .0112481    -2.44   0.015    -.0495122   -.0054205
                    2020  |  -.0283142   .0111649    -2.54   0.011    -.0501971   -.0064314
                    2021  |  -.0288205    .011029    -2.61   0.009     -.050437   -.0072041
                    2022  |  -.0306713   .0111179    -2.76   0.006    -.0524621   -.0088806
                          |
                    _cons |   .1097093   .0108324    10.13   0.000     .0884783    .1309404
--------------------------+----------------------------------------------------------------
                  sigma_u |  .04226047
                  sigma_e |  .05768624
                      rho |  .34925098   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(i.emb_typeBIN) ctitle(10) addtext(Country FE, Region FE, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *Governmental System 
. xtreg natsubnatdiff_abs i.pres_parlBIN  i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =      4,626
Group variable: country_id                      Number of groups  =        163

R-squared:                                      Obs per group:
     Within  = 0.0312                                         min =          1
     Between = 0.1840                                         avg =       28.4
     Overall = 0.1063                                         max =         33

                                                Wald chi2(42)     =     114.40
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                        (Std. err. adjusted for 163 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
           1.pres_parlBIN |  -.0010588   .0315467    -0.03   0.973    -.0628892    .0607717
                          |
                region_id |
               East Asia  |  -.0277176   .0137815    -2.01   0.044    -.0547289   -.0007064
           Latin America  |  -.0023431   .0166841    -0.14   0.888    -.0350434    .0303573
                    MENA  |   .0247161   .0144759     1.71   0.088    -.0036562    .0530883
              South Asia  |   .0272255   .0160953     1.69   0.091    -.0043207    .0587717
         South-East Asia  |   .0168079   .0146826     1.14   0.252    -.0119695    .0455852
      Sub-Saharan Africa  |   .0427342    .017296     2.47   0.013     .0088346    .0766338
           The Carribean  |  -.0136743   .0125921    -1.09   0.278    -.0383543    .0110057
             The Pacific  |   .0368792   .0319773     1.15   0.249    -.0257952    .0995537
W. Europe and N. America  |  -.0120032   .0110268    -1.09   0.276    -.0336153    .0096089
                          |
                     year |
                    1991  |  -.0129466    .007238    -1.79   0.074    -.0271329    .0012396
                    1992  |  -.0087115   .0102049    -0.85   0.393    -.0287126    .0112897
                    1993  |  -.0226964   .0105919    -2.14   0.032    -.0434562   -.0019366
                    1994  |  -.0297469   .0105173    -2.83   0.005    -.0503603   -.0091334
                    1995  |  -.0331495   .0105195    -3.15   0.002    -.0537673   -.0125317
                    1996  |  -.0319929   .0106916    -2.99   0.003    -.0529482   -.0110377
                    1997  |  -.0362418   .0104025    -3.48   0.000    -.0566303   -.0158533
                    1998  |  -.0358878   .0103824    -3.46   0.001    -.0562369   -.0155386
                    1999  |  -.0298993   .0104397    -2.86   0.004    -.0503607   -.0094379
                    2000  |   -.026776   .0110021    -2.43   0.015    -.0483398   -.0052122
                    2001  |  -.0314719   .0110487    -2.85   0.004    -.0531269   -.0098169
                    2002  |  -.0363746     .01095    -3.32   0.001    -.0578361    -.014913
                    2003  |    -.03696   .0110952    -3.33   0.001    -.0587063   -.0152137
                    2004  |  -.0366491   .0111134    -3.30   0.001     -.058431   -.0148671
                    2005  |  -.0384235   .0113103    -3.40   0.001    -.0605914   -.0162557
                    2006  |  -.0421843   .0114625    -3.68   0.000    -.0646504   -.0197183
                    2007  |  -.0457344   .0114551    -3.99   0.000     -.068186   -.0232827
                    2008  |  -.0454478   .0115796    -3.92   0.000    -.0681435   -.0227522
                    2009  |  -.0468945   .0114174    -4.11   0.000    -.0692723   -.0245167
                    2010  |  -.0471581   .0115389    -4.09   0.000     -.069774   -.0245422
                    2011  |  -.0451816   .0115956    -3.90   0.000    -.0679085   -.0224546
                    2012  |  -.0482443   .0115463    -4.18   0.000    -.0708746    -.025614
                    2013  |  -.0438284    .011697    -3.75   0.000    -.0667542   -.0209027
                    2014  |  -.0412271   .0117733    -3.50   0.000    -.0643024   -.0181518
                    2015  |  -.0418207   .0118994    -3.51   0.000     -.065143   -.0184983
                    2016  |  -.0433917   .0119314    -3.64   0.000    -.0667768   -.0200065
                    2017  |  -.0401128    .012037    -3.33   0.001     -.063705   -.0165206
                    2018  |  -.0411385   .0122078    -3.37   0.001    -.0650653   -.0172116
                    2019  |  -.0292987   .0122718    -2.39   0.017     -.053351   -.0052463
                    2020  |   -.030131   .0120355    -2.50   0.012    -.0537201   -.0065418
                    2021  |  -.0306937   .0121148    -2.53   0.011    -.0544382   -.0069492
                    2022  |  -.0325368   .0120752    -2.69   0.007    -.0562037   -.0088699
                          |
                    _cons |   .1093454   .0215606     5.07   0.000     .0670875    .1516033
--------------------------+----------------------------------------------------------------
                  sigma_u |   .0422503
                  sigma_e |  .05771358
                      rho |  .34892626   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(i.pres_parlBIN) ctitle(11) addtext(Country FE, Region FE, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *Electoral System 
. xtreg natsubnatdiff_abs i.prop_rep_bin  i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =      4,545
Group variable: country_id                      Number of groups  =        162

R-squared:                                      Obs per group:
     Within  = 0.0345                                         min =          1
     Between = 0.1815                                         avg =       28.1
     Overall = 0.0847                                         max =         33

                                                Wald chi2(42)     =     104.29
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                        (Std. err. adjusted for 162 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
           1.prop_rep_bin |    -.02058   .0129825    -1.59   0.113    -.0460252    .0048653
                          |
                region_id |
               East Asia  |  -.0412164   .0163152    -2.53   0.012    -.0731936   -.0092393
           Latin America  |   .0004916   .0098998     0.05   0.960    -.0189117     .019895
                    MENA  |   .0177764   .0124544     1.43   0.153    -.0066338    .0421867
              South Asia  |   .0168281    .017427     0.97   0.334    -.0173282    .0509845
         South-East Asia  |   .0101598   .0151477     0.67   0.502    -.0195292    .0398488
      Sub-Saharan Africa  |   .0266969   .0112071     2.38   0.017     .0047313    .0486624
           The Carribean  |  -.0168565     .01256    -1.34   0.180    -.0414737    .0077607
             The Pacific  |   .0290304   .0297884     0.97   0.330    -.0293538    .0874146
W. Europe and N. America  |  -.0090372   .0091821    -0.98   0.325    -.0270337    .0089593
                          |
                     year |
                    1991  |  -.0165152   .0077222    -2.14   0.032    -.0316505     -.00138
                    1992  |  -.0129496   .0101111    -1.28   0.200    -.0327669    .0068678
                    1993  |  -.0255791   .0104832    -2.44   0.015    -.0461258   -.0050325
                    1994  |  -.0310399   .0106088    -2.93   0.003    -.0518328   -.0102469
                    1995  |  -.0343024   .0106351    -3.23   0.001    -.0551468    -.013458
                    1996  |  -.0333565   .0108125    -3.08   0.002    -.0545485   -.0121644
                    1997  |   -.036614   .0105456    -3.47   0.001    -.0572829   -.0159451
                    1998  |  -.0354277   .0105018    -3.37   0.001    -.0560108   -.0148446
                    1999  |  -.0302404   .0106087    -2.85   0.004    -.0510329   -.0094478
                    2000  |  -.0274108   .0111679    -2.45   0.014    -.0492994   -.0055222
                    2001  |  -.0318155   .0112227    -2.83   0.005    -.0538117   -.0098194
                    2002  |  -.0369893   .0112167    -3.30   0.001    -.0589736   -.0150049
                    2003  |  -.0375514   .0114325    -3.28   0.001    -.0599588   -.0151441
                    2004  |  -.0372703   .0112851    -3.30   0.001    -.0593886    -.015152
                    2005  |  -.0384334   .0114055    -3.37   0.001    -.0607878    -.016079
                    2006  |  -.0419956    .011557    -3.63   0.000     -.064647   -.0193443
                    2007  |  -.0455456   .0115722    -3.94   0.000    -.0682267   -.0228645
                    2008  |  -.0451378   .0116495    -3.87   0.000    -.0679704   -.0223052
                    2009  |  -.0462925   .0114366    -4.05   0.000    -.0687078   -.0238771
                    2010  |   -.046928   .0116734    -4.02   0.000    -.0698075   -.0240485
                    2011  |  -.0446597   .0117342    -3.81   0.000    -.0676583   -.0216611
                    2012  |  -.0463323   .0116212    -3.99   0.000    -.0691095   -.0235551
                    2013  |  -.0426537   .0115501    -3.69   0.000    -.0652914    -.020016
                    2014  |  -.0398293    .011758    -3.39   0.001    -.0628746   -.0167841
                    2015  |  -.0402918   .0118517    -3.40   0.001    -.0635207   -.0170629
                    2016  |  -.0422716   .0119324    -3.54   0.000    -.0656587   -.0188846
                    2017  |  -.0393072   .0120382    -3.27   0.001    -.0629017   -.0157127
                    2018  |  -.0398991   .0122694    -3.25   0.001    -.0639466   -.0158515
                    2019  |  -.0284076   .0122927    -2.31   0.021    -.0525009   -.0043143
                    2020  |  -.0291272   .0120494    -2.42   0.016    -.0527436   -.0055107
                    2021  |  -.0296084   .0120662    -2.45   0.014    -.0532577   -.0059591
                    2022  |  -.0314987   .0120253    -2.62   0.009    -.0550679   -.0079295
                          |
                    _cons |   .1222076   .0150065     8.14   0.000     .0927954    .1516197
--------------------------+----------------------------------------------------------------
                  sigma_u |  .03496955
                  sigma_e |  .05732426
                      rho |  .27120988   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(i.prop_rep_bin) ctitle(12) addtext(Country FE, Region FE, Year FE, Yes) label
mydoc.doc
dir : seeout

. *
. *Saturated Model
. xtreg natsubnatdiff_abs gdppc_log e_wb_pop_log ethnic_frac terrain_rugged i.FEDERALISM_DUMMY inv_state_fsi rai_index emb_o
> verall_capacity i.emb_typeBIN i.pres_parlBIN i.prop_rep_bin i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =        293
Group variable: country_id                      Number of groups  =         50

R-squared:                                      Obs per group:
     Within  = 0.1393                                         min =          4
     Between = 0.4438                                         avg =        5.9
     Overall = 0.3200                                         max =          6

                                                Wald chi2(22)     =     140.38
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                         (Std. err. adjusted for 50 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
                gdppc_log |  -.0223206   .0199921    -1.12   0.264    -.0615044    .0168632
             e_wb_pop_log |   .0045417   .0083679     0.54   0.587    -.0118591    .0209425
              ethnic_frac |  -.0289433   .0449412    -0.64   0.520    -.1170265    .0591399
           terrain_rugged |   .0000745   .0000698     1.07   0.286    -.0000623    .0002114
       1.FEDERALISM_DUMMY |  -.0259158   .0223306    -1.16   0.246     -.069683    .0178515
            inv_state_fsi |   .0014004   .0003796     3.69   0.000     .0006565    .0021444
                rai_index |   .0015486   .0021945     0.71   0.480    -.0027524    .0058497
     emb_overall_capacity |  -.0551842   .0167992    -3.28   0.001      -.08811   -.0222584
                          |
              emb_typeBIN |
        Government/Mixed  |  -.0223201   .0182584    -1.22   0.222    -.0581059    .0134658
           1.pres_parlBIN |   .0437761   .0344657     1.27   0.204    -.0237755    .1113277
           1.prop_rep_bin |   .0060435   .0089591     0.67   0.500    -.0115159     .023603
                          |
                region_id |
               East Asia  |  -.0741803   .0393576    -1.88   0.059    -.1513198    .0029592
           Latin America  |  -.0435205   .0265001    -1.64   0.101    -.0954598    .0084189
                    MENA  |  -.0856193   .0350635    -2.44   0.015    -.1543426    -.016896
              South Asia  |   .0529202   .0409126     1.29   0.196     -.027267    .1331073
         South-East Asia  |   .0080508   .0350763     0.23   0.818    -.0606975     .076799
W. Europe and N. America  |  -.0306519     .02606    -1.18   0.240    -.0817286    .0204247
                          |
                     year |
                    2007  |  -.0051973   .0054464    -0.95   0.340    -.0158722    .0054775
                    2008  |  -.0075527   .0070757    -1.07   0.286    -.0214208    .0063154
                    2009  |  -.0147701    .008159    -1.81   0.070    -.0307615    .0012212
                    2010  |  -.0133557   .0106459    -1.25   0.210    -.0342213      .00751
                    2011  |  -.0107638    .011273    -0.95   0.340    -.0328584    .0113309
                          |
                    _cons |   .2395634   .1792369     1.34   0.181    -.1117345    .5908613
--------------------------+----------------------------------------------------------------
                  sigma_u |  .03604585
                  sigma_e |  .03193609
                      rho |  .56023361   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(gdppc_log e_wb_pop_log ethnic_frac terrain_rugged i.FEDERALISM_DUMMY inv_state_fsi ra
> i_index emb_overall_capacity i.emb_typeBIN i.pres_parlBIN i.prop_rep_bin) ctitle(13) addtext(Country FE, Region FE, Year F
> E, Yes) label
mydoc.doc
dir : seeout

. *
. *Sat Model 2
. xtreg natsubnatdiff_abs gdppc_log e_wb_pop_log ethnic_frac terrain_rugged i.FEDERALISM_DUMMY inv_state_sfi rai_index emb_o
> verall_capacity i.emb_typeBIN i.pres_parlBIN i.prop_rep_bin i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =        832
Group variable: country_id                      Number of groups  =         51

R-squared:                                      Obs per group:
     Within  = 0.1117                                         min =          4
     Between = 0.2559                                         avg =       16.3
     Overall = 0.1917                                         max =         17

                                                Wald chi2(33)     =     200.65
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                         (Std. err. adjusted for 51 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
                gdppc_log |  -.0133828   .0212066    -0.63   0.528    -.0549469    .0281813
             e_wb_pop_log |   .0004406   .0084892     0.05   0.959    -.0161978    .0170791
              ethnic_frac |  -.0060386   .0331773    -0.18   0.856    -.0710649    .0589878
           terrain_rugged |    .000111   .0000764     1.45   0.146    -.0000387    .0002607
       1.FEDERALISM_DUMMY |  -.0335433   .0220748    -1.52   0.129    -.0768092    .0097226
            inv_state_sfi |   .0023859   .0027415     0.87   0.384    -.0029873    .0077591
                rai_index |   .0030544   .0016632     1.84   0.066    -.0002055    .0063142
     emb_overall_capacity |  -.0229029   .0167207    -1.37   0.171    -.0556749    .0098692
                          |
              emb_typeBIN |
        Government/Mixed  |  -.0161226   .0180216    -0.89   0.371    -.0514442    .0191991
           1.pres_parlBIN |    .029306   .0294797     0.99   0.320    -.0284731     .087085
           1.prop_rep_bin |  -.0392869   .0242566    -1.62   0.105     -.086829    .0082552
                          |
                region_id |
               East Asia  |  -.0889691   .0403164    -2.21   0.027    -.1679877   -.0099504
           Latin America  |  -.0414354   .0270789    -1.53   0.126     -.094509    .0116383
                    MENA  |  -.0401355   .0332295    -1.21   0.227    -.1052641    .0249931
              South Asia  |   .0043354   .0469698     0.09   0.926    -.0877236    .0963945
         South-East Asia  |  -.0044191   .0374487    -0.12   0.906    -.0778171     .068979
W. Europe and N. America  |  -.0190109    .028963    -0.66   0.512    -.0757773    .0377556
                          |
                     year |
                    1996  |   .0038782    .004088     0.95   0.343    -.0041341    .0118906
                    1997  |   .0003067   .0037286     0.08   0.934    -.0070012    .0076147
                    1998  |   .0015712   .0069892     0.22   0.822    -.0121274    .0152697
                    1999  |   .0090515   .0069334     1.31   0.192    -.0045376    .0226407
                    2000  |    .013352   .0082425     1.62   0.105    -.0028031    .0295071
                    2001  |   .0078904   .0092419     0.85   0.393    -.0102234    .0260042
                    2002  |  -.0031161   .0099677    -0.31   0.755    -.0226525    .0164203
                    2003  |  -.0012145   .0100612    -0.12   0.904    -.0209342    .0185052
                    2004  |   .0098767   .0138381     0.71   0.475    -.0172454    .0369988
                    2005  |   .0101985   .0153257     0.67   0.506    -.0198394    .0402363
                    2006  |   .0009851   .0154737     0.06   0.949    -.0293427     .031313
                    2007  |   -.005923   .0161526    -0.37   0.714    -.0375815    .0257355
                    2008  |  -.0108904   .0171279    -0.64   0.525    -.0444604    .0226796
                    2009  |  -.0171031   .0173764    -0.98   0.325    -.0511602     .016954
                    2010  |  -.0153879   .0191732    -0.80   0.422    -.0529668    .0221909
                    2011  |  -.0118083   .0207281    -0.57   0.569    -.0524346    .0288179
                          |
                    _cons |   .2000453   .1720748     1.16   0.245    -.1372152    .5373057
--------------------------+----------------------------------------------------------------
                  sigma_u |  .04144506
                  sigma_e |  .04275107
                      rho |  .48449221   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. outreg2 using mydoc.doc, append keep(gdppc_log e_wb_pop_log ethnic_frac terrain_rugged i.FEDERALISM_DUMMY inv_state_sfi ra
> i_index emb_overall_capacity i.emb_typeBIN i.pres_parlBIN i.prop_rep_bin) ctitle(14) addtext(Country FE, Region FE, Year F
> E, Yes) label
mydoc.doc
dir : seeout

. *
. *Regression With RAI Subcomponents
. xtreg natsubnatdiff_abs rai_n_selfrule i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0367                                         min =         10
     Between = 0.0072                                         avg =       26.9
     Overall = 0.0242                                         max =         29

                                                F(29, 86)         =       2.11
corr(u_i, Xb) = -0.0380                         Prob > F          =     0.0042

                              (Std. err. adjusted for 87 clusters in country_id)
--------------------------------------------------------------------------------
               |               Robust
natsubnatdif~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
rai_n_selfrule |   .0007712   .0014397     0.54   0.594    -.0020909    .0036333
               |
          year |
         1991  |  -.0134316   .0117565    -1.14   0.256    -.0368026    .0099395
         1992  |  -.0134723   .0113723    -1.18   0.239    -.0360797    .0091352
         1993  |  -.0152861   .0111549    -1.37   0.174    -.0374614    .0068891
         1994  |   -.019746   .0114636    -1.72   0.089    -.0425348    .0030429
         1995  |    -.01871   .0116731    -1.60   0.113    -.0419154    .0044954
         1996  |  -.0161513   .0118234    -1.37   0.175    -.0396553    .0073528
         1997  |  -.0227042   .0115397    -1.97   0.052    -.0456444     .000236
         1998  |  -.0198824   .0115056    -1.73   0.088    -.0427547      .00299
         1999  |   -.013926   .0114803    -1.21   0.228    -.0367481    .0088961
         2000  |  -.0092168   .0130512    -0.71   0.482    -.0351616    .0167281
         2001  |  -.0157283   .0123265    -1.28   0.205    -.0402326    .0087759
         2002  |  -.0205848   .0118008    -1.74   0.085     -.044044    .0028743
         2003  |  -.0219432   .0121758    -1.80   0.075    -.0461478    .0022614
         2004  |  -.0143813   .0125733    -1.14   0.256    -.0393761    .0106135
         2005  |  -.0153411   .0130778    -1.17   0.244     -.041339    .0106568
         2006  |  -.0217271   .0126175    -1.72   0.089    -.0468099    .0033556
         2007  |  -.0329177    .012242    -2.69   0.009     -.057254   -.0085814
         2008  |  -.0366226   .0124481    -2.94   0.004    -.0613686   -.0118766
         2009  |  -.0414993    .012358    -3.36   0.001    -.0660661   -.0169324
         2010  |  -.0389447   .0124559    -3.13   0.002    -.0637062   -.0141831
         2011  |  -.0353869   .0127175    -2.78   0.007    -.0606685   -.0101053
         2012  |  -.0341922   .0128346    -2.66   0.009    -.0597065   -.0086779
         2013  |  -.0278461   .0130322    -2.14   0.035    -.0537533   -.0019388
         2014  |   -.025644    .013526    -1.90   0.061    -.0525327    .0012448
         2015  |  -.0293182   .0132974    -2.20   0.030    -.0557526   -.0028839
         2016  |  -.0296919   .0136672    -2.17   0.033    -.0568613   -.0025224
         2017  |  -.0273722   .0142993    -1.91   0.059    -.0557983    .0010538
         2018  |  -.0232092   .0148273    -1.57   0.121    -.0526848    .0062664
               |
         _cons |   .0884064   .0138536     6.38   0.000     .0608664    .1159464
---------------+----------------------------------------------------------------
       sigma_u |  .03795504
       sigma_e |  .04783734
           rho |  .38631983   (fraction of variance due to u_i)
--------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, replace keep(rai_n_selfrule) ctitle(1) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_sharedrule  i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0363                                         min =         10
     Between = 0.0000                                         avg =       26.9
     Overall = 0.0144                                         max =         29

                                                F(29, 86)         =       1.98
corr(u_i, Xb) = -0.0934                         Prob > F          =     0.0083

                                (Std. err. adjusted for 87 clusters in country_id)
----------------------------------------------------------------------------------
                 |               Robust
natsubnatdiff_~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
rai_n_sharedrule |  -.0017436   .0020636    -0.84   0.400    -.0058458    .0023587
                 |
            year |
           1991  |  -.0133293   .0116859    -1.14   0.257    -.0365601    .0099015
           1992  |   -.013385   .0113413    -1.18   0.241    -.0359308    .0091608
           1993  |  -.0150056   .0110004    -1.36   0.176    -.0368736    .0068624
           1994  |   -.019253   .0111834    -1.72   0.089    -.0414849    .0029788
           1995  |  -.0181125   .0113223    -1.60   0.113    -.0406204    .0043955
           1996  |  -.0153515   .0114113    -1.35   0.182    -.0380364    .0073334
           1997  |  -.0219005   .0111567    -1.96   0.053    -.0440792    .0002782
           1998  |  -.0191401   .0111256    -1.72   0.089     -.041257    .0029768
           1999  |  -.0130152   .0110503    -1.18   0.242    -.0349823     .008952
           2000  |  -.0081699   .0124427    -0.66   0.513    -.0329051    .0165653
           2001  |  -.0145861   .0119878    -1.22   0.227    -.0384172    .0092449
           2002  |  -.0194851   .0115642    -1.68   0.096    -.0424739    .0035037
           2003  |  -.0205602   .0119323    -1.72   0.088    -.0442809    .0031605
           2004  |  -.0127986   .0123287    -1.04   0.302    -.0373073    .0117101
           2005  |  -.0137039   .0127542    -1.07   0.286    -.0390585    .0116506
           2006  |  -.0203555   .0122594    -1.66   0.100    -.0447265    .0040155
           2007  |  -.0315309   .0119254    -2.64   0.010    -.0552378    -.007824
           2008  |  -.0351828   .0118577    -2.97   0.004    -.0587551   -.0116106
           2009  |  -.0400295   .0117714    -3.40   0.001    -.0634303   -.0166287
           2010  |   -.037552   .0119098    -3.15   0.002     -.061228   -.0138761
           2011  |  -.0338369     .01215    -2.78   0.007    -.0579903   -.0096835
           2012  |  -.0326991   .0123217    -2.65   0.009    -.0571938   -.0082043
           2013  |  -.0262933   .0126536    -2.08   0.041    -.0514478   -.0011388
           2014  |  -.0240009   .0128934    -1.86   0.066     -.049632    .0016303
           2015  |  -.0275752   .0126033    -2.19   0.031    -.0526298   -.0025206
           2016  |   -.027972    .012813    -2.18   0.032    -.0534434   -.0025005
           2017  |  -.0255943   .0133679    -1.91   0.059    -.0521689    .0009803
           2018  |  -.0211914   .0139228    -1.52   0.132     -.048869    .0064862
                 |
           _cons |   .0974582    .011783     8.27   0.000     .0740344    .1208819
-----------------+----------------------------------------------------------------
         sigma_u |  .03845211
         sigma_e |   .0478462
             rho |  .39241878   (fraction of variance due to u_i)
----------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_sharedrule) ctitle(2) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_selfrule rai_n_sharedrule i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0381                                         min =         10
     Between = 0.0058                                         avg =       26.9
     Overall = 0.0212                                         max =         29

                                                F(30, 86)         =       2.09
corr(u_i, Xb) = -0.0847                         Prob > F          =     0.0043

                                (Std. err. adjusted for 87 clusters in country_id)
----------------------------------------------------------------------------------
                 |               Robust
natsubnatdiff_~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
  rai_n_selfrule |   .0011416   .0017666     0.65   0.520    -.0023703    .0046534
rai_n_sharedrule |  -.0029317   .0031668    -0.93   0.357     -.009227    .0033637
                 |
            year |
           1991  |  -.0135177   .0117525    -1.15   0.253     -.036881    .0098455
           1992  |   -.013447    .011337    -1.19   0.239    -.0359843    .0090903
           1993  |  -.0154685   .0111719    -1.38   0.170    -.0376775    .0067406
           1994  |  -.0199996   .0115333    -1.73   0.086     -.042927    .0029278
           1995  |  -.0191231   .0117711    -1.62   0.108    -.0425232     .004277
           1996  |   -.016365   .0118517    -1.38   0.171    -.0399255    .0071954
           1997  |  -.0228477   .0115358    -1.98   0.051    -.0457802    .0000847
           1998  |  -.0201779   .0115531    -1.75   0.084    -.0431447    .0027889
           1999  |  -.0143468   .0115661    -1.24   0.218    -.0373395    .0086459
           2000  |  -.0096767   .0131788    -0.73   0.465    -.0358753     .016522
           2001  |  -.0162652   .0124094    -1.31   0.193    -.0409342    .0084038
           2002  |  -.0212894   .0118804    -1.79   0.077    -.0449068    .0023281
           2003  |  -.0224377   .0122237    -1.84   0.070    -.0467375    .0018622
           2004  |   -.014803   .0126281    -1.17   0.244    -.0399068    .0103009
           2005  |   -.015776    .013123    -1.20   0.233    -.0418637    .0103117
           2006  |  -.0223331   .0127281    -1.75   0.083    -.0476358    .0029696
           2007  |   -.033524   .0123469    -2.72   0.008    -.0580689   -.0089791
           2008  |  -.0370676     .01254    -2.96   0.004    -.0619963   -.0121389
           2009  |  -.0419797     .01246    -3.37   0.001    -.0667494   -.0172101
           2010  |  -.0395762   .0126402    -3.13   0.002    -.0647041   -.0144483
           2011  |  -.0358768   .0128502    -2.79   0.006    -.0614222   -.0103314
           2012  |  -.0346548   .0129421    -2.68   0.009    -.0603828   -.0089268
           2013  |  -.0283469   .0131879    -2.15   0.034    -.0545636   -.0021303
           2014  |   -.026143   .0136762    -1.91   0.059    -.0533305    .0010445
           2015  |  -.0295672   .0133578    -2.21   0.030    -.0561216   -.0030129
           2016  |  -.0299355   .0137248    -2.18   0.032    -.0572194   -.0026516
           2017  |  -.0275807   .0143048    -1.93   0.057    -.0560177    .0008562
           2018  |  -.0232187   .0147044    -1.58   0.118    -.0524501    .0060128
                 |
           _cons |   .0912952   .0127047     7.19   0.000      .066039    .1165514
-----------------+----------------------------------------------------------------
         sigma_u |  .03817708
         sigma_e |  .04781252
             rho |  .38933596   (fraction of variance due to u_i)
----------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_selfrule rai_n_sharedrule) ctitle(3) addtext(Country FE, Yes, Year FE, Yes) la
> bel
mydoc2.doc
dir : seeout

. *SUBCOMPONENTS
. xtreg natsubnatdiff_abs rai_n_instdepth i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0359                                         min =         10
     Between = 0.0154                                         avg =       26.9
     Overall = 0.0274                                         max =         29

                                                F(29, 86)         =       2.02
corr(u_i, Xb) = 0.0136                          Prob > F          =     0.0068

                               (Std. err. adjusted for 87 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
natsubnatdiff~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rai_n_instdepth |   .0017711   .0064029     0.28   0.783    -.0109576    .0144997
                |
           year |
          1991  |  -.0133807   .0117743    -1.14   0.259    -.0367873    .0100258
          1992  |  -.0134036   .0113469    -1.18   0.241    -.0359605    .0091533
          1993  |  -.0150616   .0110992    -1.36   0.178    -.0371261    .0070029
          1994  |  -.0193714   .0113544    -1.71   0.092    -.0419431    .0032003
          1995  |  -.0182666   .0115999    -1.57   0.119    -.0413264    .0047931
          1996  |  -.0156875   .0117576    -1.33   0.186    -.0390608    .0076857
          1997  |  -.0222448   .0114704    -1.94   0.056    -.0450472    .0005577
          1998  |  -.0193895   .0113799    -1.70   0.092    -.0420119    .0032329
          1999  |  -.0133335   .0113663    -1.17   0.244     -.035929     .009262
          2000  |  -.0085931   .0129497    -0.66   0.509    -.0343364    .0171501
          2001  |  -.0150611   .0124122    -1.21   0.228    -.0397357    .0096135
          2002  |   -.019877   .0119054    -1.67   0.099    -.0435442    .0037901
          2003  |   -.021137   .0123318    -1.71   0.090    -.0456518    .0033779
          2004  |  -.0134919   .0126934    -1.06   0.291    -.0387254    .0117417
          2005  |  -.0144025   .0132515    -1.09   0.280    -.0407457    .0119407
          2006  |  -.0208747   .0127211    -1.64   0.104    -.0461634    .0044141
          2007  |   -.032085   .0123677    -2.59   0.011    -.0566711   -.0074988
          2008  |  -.0358208   .0124395    -2.88   0.005    -.0605496    -.011092
          2009  |  -.0406954   .0123603    -3.29   0.001    -.0652668   -.0161239
          2010  |  -.0380772   .0123725    -3.08   0.003    -.0626729   -.0134815
          2011  |  -.0345452   .0127483    -2.71   0.008    -.0598881   -.0092024
          2012  |  -.0333394   .0128148    -2.60   0.011    -.0588145   -.0078644
          2013  |  -.0269264    .013028    -2.07   0.042    -.0528253   -.0010276
          2014  |  -.0246862    .013491    -1.83   0.071    -.0515053    .0021329
          2015  |  -.0283837   .0132889    -2.14   0.036    -.0548011   -.0019662
          2016  |   -.028776   .0135304    -2.13   0.036    -.0556735   -.0018784
          2017  |  -.0264238   .0141968    -1.86   0.066     -.054646    .0017984
          2018  |  -.0222282   .0147918    -1.50   0.137    -.0516332    .0071769
                |
          _cons |   .0906656   .0142845     6.35   0.000      .062269    .1190623
----------------+----------------------------------------------------------------
        sigma_u |  .03769479
        sigma_e |  .04785599
            rho |  .38287831   (fraction of variance due to u_i)
---------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_instdepth) ctitle(4) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_policyautonomy i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0376                                         min =         10
     Between = 0.0031                                         avg =       26.9
     Overall = 0.0213                                         max =         29

                                                F(29, 86)         =       2.13
corr(u_i, Xb) = -0.0618                         Prob > F          =     0.0038

                                    (Std. err. adjusted for 87 clusters in country_id)
--------------------------------------------------------------------------------------
                     |               Robust
   natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
rai_n_policyautonomy |   .0040692   .0050977     0.80   0.427    -.0060647    .0142031
                     |
                year |
               1991  |   -.013535   .0117269    -1.15   0.252    -.0368472    .0097773
               1992  |  -.0136101   .0113931    -1.19   0.236    -.0362588    .0090385
               1993  |  -.0155428   .0111633    -1.39   0.167    -.0377347    .0066492
               1994  |   -.020067   .0114313    -1.76   0.083    -.0427917    .0026577
               1995  |   -.019025   .0115894    -1.64   0.104     -.042064    .0040139
               1996  |   -.016515   .0117299    -1.41   0.163    -.0398333    .0068033
               1997  |   -.023114   .0114536    -2.02   0.047    -.0458829   -.0003451
               1998  |  -.0203151   .0114624    -1.77   0.080    -.0431016    .0024714
               1999  |  -.0144477   .0114223    -1.26   0.209    -.0371544     .008259
               2000  |   -.009804   .0130113    -0.75   0.453    -.0356696    .0160616
               2001  |   -.016398   .0122561    -1.34   0.184    -.0407623    .0079662
               2002  |  -.0212822   .0117619    -1.81   0.074    -.0446642    .0020997
               2003  |  -.0226554   .0120903    -1.87   0.064      -.04669    .0013793
               2004  |  -.0152433   .0125436    -1.22   0.228    -.0401791    .0096924
               2005  |  -.0162404   .0130106    -1.25   0.215    -.0421045    .0096238
               2006  |  -.0225782   .0124933    -1.81   0.074    -.0474142    .0022577
               2007  |  -.0337889   .0121598    -2.78   0.007    -.0579617   -.0096161
               2008  |  -.0375006   .0123754    -3.03   0.003     -.062102   -.0128991
               2009  |  -.0423536   .0123384    -3.43   0.001    -.0668816   -.0178256
               2010  |  -.0397868     .01246    -3.19   0.002    -.0645564   -.0150171
               2011  |  -.0362243    .012681    -2.86   0.005    -.0614332   -.0110153
               2012  |  -.0350539   .0127714    -2.74   0.007    -.0604425   -.0096652
               2013  |  -.0286824   .0129115    -2.22   0.029    -.0543495   -.0030152
               2014  |  -.0265259    .013391    -1.98   0.051    -.0531464    .0000945
               2015  |  -.0302041   .0131082    -2.30   0.024    -.0562625   -.0041458
               2016  |  -.0304997   .0135667    -2.25   0.027    -.0574694     -.00353
               2017  |  -.0281945    .014157    -1.99   0.050    -.0563377   -.0000512
               2018  |  -.0240648   .0147282    -1.63   0.106    -.0533434    .0052138
                     |
               _cons |   .0892751   .0116173     7.68   0.000     .0661806    .1123696
---------------------+----------------------------------------------------------------
             sigma_u |  .03817915
             sigma_e |  .04781472
                 rho |  .38933987   (fraction of variance due to u_i)
--------------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_policyautonomy) ctitle(5) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_fiscalautonomy i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0384                                         min =         10
     Between = 0.0069                                         avg =       26.9
     Overall = 0.0222                                         max =         29

                                                F(29, 86)         =       2.30
corr(u_i, Xb) = -0.1285                         Prob > F          =     0.0016

                                    (Std. err. adjusted for 87 clusters in country_id)
--------------------------------------------------------------------------------------
                     |               Robust
   natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
rai_n_fiscalautonomy |   .0067351    .004527     1.49   0.140    -.0022643    .0157346
                     |
                year |
               1991  |  -.0132611    .011729    -1.13   0.261    -.0365776    .0100555
               1992  |  -.0132994   .0113415    -1.17   0.244    -.0358456    .0092469
               1993  |  -.0149737   .0110064    -1.36   0.177    -.0368538    .0069063
               1994  |  -.0194948   .0112008    -1.74   0.085    -.0417612    .0027717
               1995  |  -.0185177   .0113324    -1.63   0.106    -.0410458    .0040103
               1996  |  -.0158717    .011435    -1.39   0.169    -.0386038    .0068604
               1997  |  -.0225693   .0111789    -2.02   0.047    -.0447921   -.0003465
               1998  |  -.0198922   .0111755    -1.78   0.079    -.0421083     .002324
               1999  |  -.0137926    .011087    -1.24   0.217    -.0358329    .0082477
               2000  |  -.0091898   .0124281    -0.74   0.462    -.0338961    .0155164
               2001  |  -.0157799   .0119857    -1.32   0.191    -.0396066    .0080469
               2002  |  -.0204788   .0115356    -1.78   0.079    -.0434108    .0024533
               2003  |  -.0218882   .0118217    -1.85   0.068     -.045389    .0016126
               2004  |   -.014303   .0122432    -1.17   0.246    -.0386416    .0100356
               2005  |   -.015287   .0126922    -1.20   0.232    -.0405183    .0099444
               2006  |  -.0215163   .0121814    -1.77   0.081    -.0457321    .0026994
               2007  |  -.0325113    .011843    -2.75   0.007    -.0560544   -.0089682
               2008  |  -.0362278   .0118718    -3.05   0.003    -.0598281   -.0126274
               2009  |  -.0409728   .0118137    -3.47   0.001    -.0644577   -.0174879
               2010  |  -.0385949   .0118999    -3.24   0.002    -.0622511   -.0149387
               2011  |  -.0349782   .0121238    -2.89   0.005    -.0590794   -.0108769
               2012  |  -.0338402    .012342    -2.74   0.007    -.0583753    -.009305
               2013  |  -.0274859   .0126112    -2.18   0.032    -.0525562   -.0024156
               2014  |  -.0253088   .0128959    -1.96   0.053     -.050945    .0003275
               2015  |  -.0289414   .0126385    -2.29   0.024     -.054066   -.0038168
               2016  |  -.0293283   .0128733    -2.28   0.025    -.0549197    -.003737
               2017  |  -.0269872   .0134345    -2.01   0.048    -.0536942   -.0002802
               2018  |  -.0225934   .0139731    -1.62   0.110    -.0503709    .0051842
                     |
               _cons |   .0875083   .0115193     7.60   0.000     .0646087     .110408
---------------------+----------------------------------------------------------------
             sigma_u |  .03854732
             sigma_e |  .04779421
                 rho |  .39411775   (fraction of variance due to u_i)
--------------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_fiscalautonomy) ctitle(6) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_borrowautonomy i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0369                                         min =         10
     Between = 0.0000                                         avg =       26.9
     Overall = 0.0152                                         max =         29

                                                F(29, 86)         =       1.94
corr(u_i, Xb) = -0.0794                         Prob > F          =     0.0099

                                    (Std. err. adjusted for 87 clusters in country_id)
--------------------------------------------------------------------------------------
                     |               Robust
   natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
rai_n_borrowautonomy |  -.0044771   .0056157    -0.80   0.427    -.0156407    .0066864
                     |
                year |
               1991  |  -.0132017   .0117041    -1.13   0.262    -.0364687    .0100653
               1992  |  -.0133341   .0113931    -1.17   0.245     -.035983    .0093147
               1993  |  -.0147206   .0110766    -1.33   0.187    -.0367401     .007299
               1994  |  -.0188781   .0112788    -1.67   0.098    -.0412997    .0035435
               1995  |  -.0176309   .0114205    -1.54   0.126    -.0403341    .0050723
               1996  |   -.015042   .0115196    -1.31   0.195    -.0379422    .0078581
               1997  |  -.0217945   .0112433    -1.94   0.056    -.0441454    .0005564
               1998  |  -.0189012   .0112096    -1.69   0.095    -.0411851    .0033826
               1999  |  -.0126302   .0111534    -1.13   0.261    -.0348025    .0095421
               2000  |  -.0079986   .0125489    -0.64   0.526    -.0329449    .0169478
               2001  |  -.0142942   .0120713    -1.18   0.240    -.0382911    .0097026
               2002  |  -.0191147   .0116358    -1.64   0.104    -.0422459    .0040166
               2003  |  -.0202429   .0120143    -1.68   0.096    -.0441266    .0036407
               2004  |  -.0124622   .0124121    -1.00   0.318    -.0371366    .0122123
               2005  |  -.0133728    .012834    -1.04   0.300    -.0388858    .0121403
               2006  |   -.019963   .0123522    -1.62   0.110    -.0445183    .0045924
               2007  |  -.0311684   .0120086    -2.60   0.011    -.0550407   -.0072961
               2008  |  -.0349158   .0119497    -2.92   0.004    -.0586709   -.0111607
               2009  |  -.0396996   .0118286    -3.36   0.001     -.063214   -.0161851
               2010  |  -.0370759   .0119529    -3.10   0.003    -.0608375   -.0133142
               2011  |  -.0335144   .0121716    -2.75   0.007    -.0577107    -.009318
               2012  |  -.0324139   .0123686    -2.62   0.010    -.0570018    -.007826
               2013  |   -.025915   .0126811    -2.04   0.044    -.0511242   -.0007058
               2014  |  -.0236557   .0129796    -1.82   0.072    -.0494582    .0021469
               2015  |  -.0273665   .0127254    -2.15   0.034    -.0526638   -.0020692
               2016  |  -.0277722   .0129397    -2.15   0.035    -.0534955   -.0020489
               2017  |  -.0254222   .0135405    -1.88   0.064    -.0523398    .0014954
               2018  |  -.0211631    .014113    -1.50   0.137    -.0492189    .0068927
                     |
               _cons |   .0984243   .0116286     8.46   0.000     .0753074    .1215411
---------------------+----------------------------------------------------------------
             sigma_u |  .03835189
             sigma_e |  .04783211
                 rho |  .39131534   (fraction of variance due to u_i)
--------------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_borrowautonomy) ctitle(7) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_representation_11 i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0367                                         min =         10
     Between = 0.0065                                         avg =       26.9
     Overall = 0.0236                                         max =         29

                                                F(29, 86)         =       2.11
corr(u_i, Xb) = -0.0288                         Prob > F          =     0.0042

                                       (Std. err. adjusted for 87 clusters in country_id)
-----------------------------------------------------------------------------------------
                        |               Robust
      natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
rai_n_representation_11 |   .0020874   .0049592     0.42   0.675    -.0077712     .011946
                        |
                   year |
                  1991  |  -.0134043   .0117497    -1.14   0.257     -.036762    .0099534
                  1992  |  -.0134804   .0113899    -1.18   0.240    -.0361228    .0091621
                  1993  |  -.0153039   .0112223    -1.36   0.176    -.0376131    .0070054
                  1994  |  -.0197795   .0115909    -1.71   0.092    -.0428215    .0032625
                  1995  |    -.01874   .0118305    -1.58   0.117    -.0422583    .0047784
                  1996  |  -.0162048   .0120324    -1.35   0.182    -.0401244    .0077148
                  1997  |  -.0227722   .0117621    -1.94   0.056    -.0461545    .0006101
                  1998  |  -.0199056   .0116909    -1.70   0.092    -.0431463    .0033351
                  1999  |  -.0139517   .0116976    -1.19   0.236    -.0372058    .0093025
                  2000  |  -.0092713   .0133758    -0.69   0.490    -.0358614    .0173189
                  2001  |  -.0156876   .0124518    -1.26   0.211    -.0404409    .0090657
                  2002  |  -.0206172   .0119456    -1.73   0.088    -.0443643    .0031299
                  2003  |  -.0219822   .0123389    -1.78   0.078    -.0465111    .0025466
                  2004  |  -.0143676   .0127434    -1.13   0.263    -.0397007    .0109655
                  2005  |  -.0153445   .0132308    -1.16   0.249    -.0416464    .0109574
                  2006  |  -.0217752   .0128485    -1.69   0.094    -.0473172    .0037668
                  2007  |   -.033002   .0124543    -2.65   0.010    -.0577603   -.0082436
                  2008  |  -.0366546   .0127124    -2.88   0.005     -.061926   -.0113832
                  2009  |  -.0415495   .0126247    -3.29   0.001    -.0666466   -.0164524
                  2010  |  -.0389778   .0126921    -3.07   0.003    -.0642089   -.0137467
                  2011  |  -.0354184   .0129994    -2.72   0.008    -.0612603   -.0095764
                  2012  |  -.0342409   .0131025    -2.61   0.011    -.0602878    -.008194
                  2013  |  -.0279287   .0133535    -2.09   0.039    -.0544747   -.0013827
                  2014  |  -.0257361    .013922    -1.85   0.068    -.0534121    .0019398
                  2015  |  -.0293941   .0136831    -2.15   0.035    -.0565952   -.0021931
                  2016  |  -.0297927   .0140321    -2.12   0.037    -.0576875   -.0018978
                  2017  |  -.0275018   .0146945    -1.87   0.065    -.0567135    .0017099
                  2018  |  -.0234042    .015306    -1.53   0.130    -.0538315    .0070231
                        |
                  _cons |   .0892597   .0144553     6.17   0.000     .0605235    .1179959
------------------------+----------------------------------------------------------------
                sigma_u |  .03791864
                sigma_e |  .04783601
                    rho |  .38587815   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_representation_11) ctitle(8) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_lawmaking_12 i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0365                                         min =         10
     Between = 0.0006                                         avg =       26.9
     Overall = 0.0146                                         max =         29

                                                F(29, 86)         =       1.99
corr(u_i, Xb) = -0.0798                         Prob > F          =     0.0077

                                  (Std. err. adjusted for 87 clusters in country_id)
------------------------------------------------------------------------------------
                   |               Robust
 natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
rai_n_lawmaking_12 |  -.0074146   .0074299    -1.00   0.321    -.0221847    .0073555
                   |
              year |
             1991  |  -.0133567   .0116858    -1.14   0.256    -.0365873    .0098739
             1992  |  -.0134855   .0113497    -1.19   0.238    -.0360481     .009077
             1993  |  -.0151261   .0109976    -1.38   0.173    -.0369887    .0067364
             1994  |  -.0193815   .0111962    -1.73   0.087    -.0416389    .0028759
             1995  |  -.0180401   .0113106    -1.59   0.114    -.0405248    .0044447
             1996  |   -.015231   .0114008    -1.34   0.185    -.0378951    .0074331
             1997  |  -.0218705   .0111611    -1.96   0.053     -.044058     .000317
             1998  |  -.0190262   .0111159    -1.71   0.091    -.0411237    .0030714
             1999  |  -.0128747   .0110442    -1.17   0.247    -.0348298    .0090803
             2000  |  -.0081263   .0124524    -0.65   0.516    -.0328808    .0166281
             2001  |  -.0146658   .0119987    -1.22   0.225    -.0385184    .0091868
             2002  |  -.0195987   .0115777    -1.69   0.094    -.0426144     .003417
             2003  |  -.0206132   .0119422    -1.73   0.088    -.0443536    .0031271
             2004  |  -.0128381   .0123364    -1.04   0.301     -.037362    .0116858
             2005  |  -.0137448   .0127642    -1.08   0.285    -.0391192    .0116296
             2006  |  -.0204791   .0122772    -1.67   0.099    -.0448853    .0039271
             2007  |  -.0316549   .0119286    -2.65   0.009    -.0553683   -.0079415
             2008  |  -.0353077   .0118688    -2.97   0.004    -.0589021   -.0117134
             2009  |  -.0401855   .0117866    -3.41   0.001    -.0636164   -.0167545
             2010  |  -.0376503   .0119124    -3.16   0.002    -.0613312   -.0139693
             2011  |  -.0339492   .0121618    -2.79   0.006    -.0581261   -.0097723
             2012  |  -.0328114   .0123449    -2.66   0.009    -.0573523   -.0082705
             2013  |  -.0264362   .0126703    -2.09   0.040    -.0516239   -.0012486
             2014  |  -.0241675   .0129288    -1.87   0.065    -.0498691    .0015341
             2015  |  -.0278855   .0126683    -2.20   0.030    -.0530692   -.0027018
             2016  |  -.0283415   .0128639    -2.20   0.030    -.0539141   -.0027689
             2017  |   -.026033   .0134199    -1.94   0.056    -.0527108    .0006449
             2018  |  -.0216024   .0139519    -1.55   0.125    -.0493378    .0061331
                   |
             _cons |   .0970453   .0113698     8.54   0.000     .0744428    .1196478
-------------------+----------------------------------------------------------------
           sigma_u |  .03842897
           sigma_e |  .04784077
               rho |  .39218586   (fraction of variance due to u_i)
------------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_lawmaking_12) ctitle(9) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_execcontrol i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0359                                         min =         10
     Between = 0.0005                                         avg =       26.9
     Overall = 0.0212                                         max =         29

                                                F(29, 86)         =       1.97
corr(u_i, Xb) = -0.0191                         Prob > F          =     0.0087

                                 (Std. err. adjusted for 87 clusters in country_id)
-----------------------------------------------------------------------------------
                  |               Robust
natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
rai_n_execcontrol |   .0047331   .0140118     0.34   0.736    -.0231214    .0325876
                  |
             year |
            1991  |  -.0132916   .0117034    -1.14   0.259    -.0365571     .009974
            1992  |  -.0133874   .0113649    -1.18   0.242    -.0359801    .0092053
            1993  |  -.0149151   .0110326    -1.35   0.180    -.0368472    .0070171
            1994  |  -.0192049   .0112047    -1.71   0.090    -.0414791    .0030694
            1995  |  -.0180146   .0113451    -1.59   0.116    -.0405679    .0045386
            1996  |  -.0154009   .0114423    -1.35   0.182    -.0381475    .0073457
            1997  |  -.0220439    .011194    -1.97   0.052    -.0442969     .000209
            1998  |  -.0191996   .0111527    -1.72   0.089    -.0413705    .0029713
            1999  |  -.0130619   .0110654    -1.18   0.241    -.0350593    .0089354
            2000  |  -.0083024   .0124673    -0.67   0.507    -.0330866    .0164818
            2001  |   -.014704   .0120175    -1.22   0.224    -.0385941     .009186
            2002  |   -.019468   .0116302    -1.67   0.098    -.0425881    .0036521
            2003  |   -.020759   .0119867    -1.73   0.087    -.0445878    .0030698
            2004  |  -.0131229   .0123998    -1.06   0.293    -.0377729    .0115271
            2005  |  -.0140341   .0128483    -1.09   0.278    -.0395756    .0115075
            2006  |  -.0206002   .0123937    -1.66   0.100     -.045238    .0040376
            2007  |  -.0317813   .0120661    -2.63   0.010    -.0557678   -.0077948
            2008  |   -.035586   .0120424    -2.96   0.004    -.0595255   -.0116466
            2009  |  -.0404222   .0119509    -3.38   0.001    -.0641798   -.0166645
            2010  |  -.0378663   .0120644    -3.14   0.002    -.0618495   -.0138831
            2011  |  -.0342515   .0123309    -2.78   0.007    -.0587647   -.0097384
            2012  |  -.0331137   .0124525    -2.66   0.009    -.0578684    -.008359
            2013  |  -.0267046   .0127101    -2.10   0.039    -.0519715   -.0014378
            2014  |  -.0244338   .0129484    -1.89   0.063    -.0501744    .0013067
            2015  |  -.0281999   .0127555    -2.21   0.030     -.053557   -.0028428
            2016  |  -.0285985   .0130356    -2.19   0.031    -.0545124   -.0026845
            2017  |  -.0263002   .0136384    -1.93   0.057    -.0534124    .0008121
            2018  |  -.0220445   .0142901    -1.54   0.127    -.0504523    .0063634
                  |
            _cons |   .0929488   .0109889     8.46   0.000     .0711035     .114794
------------------+----------------------------------------------------------------
          sigma_u |  .03805033
          sigma_e |  .04785668
              rho |  .38731757   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_execcontrol) ctitle(10) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_fiscalcontrol i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0360                                         min =         10
     Between = 0.0019                                         avg =       26.9
     Overall = 0.0220                                         max =         29

                                                F(29, 86)         =       1.97
corr(u_i, Xb) = -0.0171                         Prob > F          =     0.0086

                                   (Std. err. adjusted for 87 clusters in country_id)
-------------------------------------------------------------------------------------
                    |               Robust
  natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
rai_n_fiscalcontrol |  -.0053225   .0088235    -0.60   0.548     -.022863     .012218
                    |
               year |
              1991  |  -.0132735   .0116981    -1.13   0.260    -.0365285    .0099815
              1992  |  -.0132953   .0113585    -1.17   0.245    -.0358752    .0092847
              1993  |  -.0149217   .0110102    -1.36   0.179    -.0368092    .0069657
              1994  |  -.0191772   .0111912    -1.71   0.090    -.0414245    .0030701
              1995  |  -.0180508   .0113314    -1.59   0.115    -.0405769    .0044753
              1996  |  -.0153528   .0114461    -1.34   0.183    -.0381068    .0074012
              1997  |  -.0218676   .0112103    -1.95   0.054    -.0441529    .0004177
              1998  |  -.0190201   .0111688    -1.70   0.092     -.041223    .0031828
              1999  |  -.0128089   .0110903    -1.15   0.251    -.0348556    .0092378
              2000  |  -.0079745   .0124982    -0.64   0.525    -.0328201    .0168712
              2001  |  -.0143765   .0120414    -1.19   0.236    -.0383139     .009561
              2002  |  -.0191877   .0116406    -1.65   0.103    -.0423284     .003953
              2003  |  -.0203407   .0120131    -1.69   0.094     -.044222    .0035405
              2004  |  -.0126038   .0124229    -1.01   0.313    -.0372997    .0120922
              2005  |  -.0135087   .0128486    -1.05   0.296    -.0390509    .0120335
              2006  |  -.0201548   .0123162    -1.64   0.105    -.0446386    .0043289
              2007  |  -.0313331   .0119663    -2.62   0.010    -.0551212   -.0075449
              2008  |  -.0350719    .011909    -2.94   0.004    -.0587461   -.0113976
              2009  |  -.0399092   .0118149    -3.38   0.001    -.0633964   -.0164219
              2010  |  -.0373932   .0119238    -3.14   0.002    -.0610968   -.0136895
              2011  |  -.0337173   .0121882    -2.77   0.007    -.0579467    -.009488
              2012  |  -.0325795   .0123275    -2.64   0.010    -.0570857   -.0080733
              2013  |  -.0261686   .0126627    -2.07   0.042    -.0513412    -.000996
              2014  |   -.023897   .0129022    -1.85   0.067    -.0495457    .0017517
              2015  |  -.0275474    .012653    -2.18   0.032    -.0527007   -.0023941
              2016  |  -.0280013   .0128743    -2.17   0.032    -.0535946    -.002408
              2017  |  -.0255907   .0134814    -1.90   0.061    -.0523908    .0012094
              2018  |  -.0213324   .0140747    -1.52   0.133     -.049312    .0066472
                    |
              _cons |   .0952896   .0107262     8.88   0.000     .0739665    .1166126
--------------------+----------------------------------------------------------------
            sigma_u |  .03798201
            sigma_e |  .04785255
                rho |   .3865059   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_fiscalcontrol) ctitle(11) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_borrowcontrol i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0358                                         min =         10
     Between = 0.0026                                         avg =       26.9
     Overall = 0.0228                                         max =         29

                                                F(29, 86)         =       2.03
corr(u_i, Xb) = 0.0005                          Prob > F          =     0.0062

                                   (Std. err. adjusted for 87 clusters in country_id)
-------------------------------------------------------------------------------------
                    |               Robust
  natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
rai_n_borrowcontrol |  -.0018474   .0099018    -0.19   0.852    -.0215315    .0178368
                    |
               year |
              1991  |   -.013314    .011704    -1.14   0.258    -.0365808    .0099528
              1992  |  -.0134069    .011364    -1.18   0.241    -.0359978     .009184
              1993  |  -.0149708   .0110229    -1.36   0.178    -.0368836    .0069421
              1994  |  -.0192289   .0112006    -1.72   0.090    -.0414949    .0030372
              1995  |   -.017991   .0113513    -1.58   0.117    -.0405565    .0045746
              1996  |  -.0153649   .0114607    -1.34   0.184     -.038148    .0074181
              1997  |  -.0219462   .0112199    -1.96   0.054    -.0442507    .0003583
              1998  |  -.0191008    .011186    -1.71   0.091    -.0413379    .0031363
              1999  |  -.0129325   .0111025    -1.16   0.247    -.0350037    .0091386
              2000  |  -.0080925   .0124987    -0.65   0.519    -.0329392    .0167541
              2001  |  -.0144944   .0120526    -1.20   0.232    -.0384542    .0094654
              2002  |  -.0192882   .0116819    -1.65   0.102     -.042511    .0039346
              2003  |  -.0205235   .0120465    -1.70   0.092    -.0444712    .0034242
              2004  |  -.0128171   .0124574    -1.03   0.306    -.0375817    .0119474
              2005  |  -.0137286   .0129083    -1.06   0.291    -.0393895    .0119323
              2006  |  -.0202406   .0123782    -1.64   0.106    -.0448477    .0043666
              2007  |  -.0314191   .0120359    -2.61   0.011    -.0553457   -.0074925
              2008  |  -.0351117   .0120517    -2.91   0.005    -.0590697   -.0111536
              2009  |  -.0399496   .0119903    -3.33   0.001    -.0637854   -.0161137
              2010  |  -.0373417   .0121611    -3.07   0.003    -.0615171   -.0131663
              2011  |   -.033727   .0124303    -2.71   0.008    -.0584376   -.0090163
              2012  |  -.0325891   .0126493    -2.58   0.012     -.057735   -.0074432
              2013  |   -.026178   .0128827    -2.03   0.045    -.0517879   -.0005681
              2014  |  -.0239062   .0131465    -1.82   0.072    -.0500406    .0022282
              2015  |  -.0275979   .0129423    -2.13   0.036    -.0533264   -.0018694
              2016  |  -.0280109   .0131092    -2.14   0.035    -.0540712   -.0019506
              2017  |  -.0256645   .0136556    -1.88   0.064    -.0528109    .0014818
              2018  |   -.021384   .0142896    -1.50   0.138    -.0497907    .0070228
                    |
              _cons |   .0942217   .0106021     8.89   0.000     .0731455     .115298
--------------------+----------------------------------------------------------------
            sigma_u |  .03792076
            sigma_e |  .04785948
                rho |  .38567211   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_borrowcontrol) ctitle(12) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_constitutional i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0361                                         min =         10
     Between = 0.0003                                         avg =       26.9
     Overall = 0.0171                                         max =         29

                                                F(29, 86)         =       2.04
corr(u_i, Xb) = -0.0484                         Prob > F          =     0.0060

                                    (Std. err. adjusted for 87 clusters in country_id)
--------------------------------------------------------------------------------------
                     |               Robust
   natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
rai_n_constitutional |  -.0022086   .0030472    -0.72   0.471    -.0082662     .003849
                     |
                year |
               1991  |  -.0133249   .0116893    -1.14   0.257    -.0365624    .0099127
               1992  |  -.0134039   .0113454    -1.18   0.241    -.0359578    .0091501
               1993  |   -.014978   .0110043    -1.36   0.177    -.0368539    .0068979
               1994  |  -.0192434   .0111823    -1.72   0.089    -.0414731    .0029862
               1995  |  -.0181907   .0113479    -1.60   0.113    -.0407496    .0043682
               1996  |  -.0154865   .0114344    -1.35   0.179    -.0382174    .0072444
               1997  |  -.0220671   .0111857    -1.97   0.052    -.0443035    .0001693
               1998  |  -.0193318   .0111765    -1.73   0.087      -.04155    .0028863
               1999  |  -.0132724   .0111037    -1.20   0.235    -.0353459     .008801
               2000  |  -.0084417   .0124876    -0.68   0.501    -.0332662    .0163828
               2001  |  -.0148206   .0120371    -1.23   0.222    -.0387495    .0091083
               2002  |  -.0197065   .0116331    -1.69   0.094    -.0428323    .0034193
               2003  |  -.0208646   .0119999    -1.74   0.086    -.0447196    .0029905
               2004  |   -.013181   .0124066    -1.06   0.291    -.0378444    .0114825
               2005  |  -.0140886   .0128267    -1.10   0.275    -.0395873    .0114101
               2006  |  -.0206824   .0123617    -1.67   0.098    -.0452566    .0038917
               2007  |  -.0318592   .0120333    -2.65   0.010    -.0557807   -.0079377
               2008  |  -.0355991   .0120032    -2.97   0.004    -.0594607   -.0117375
               2009  |  -.0404345   .0119174    -3.39   0.001    -.0641255   -.0167434
               2010  |  -.0380073   .0121013    -3.14   0.002    -.0620639   -.0139508
               2011  |  -.0343165   .0123055    -2.79   0.007     -.058779   -.0098539
               2012  |  -.0331786   .0125101    -2.65   0.010    -.0580479   -.0083093
               2013  |  -.0267655   .0127959    -2.09   0.039    -.0522029   -.0013281
               2014  |  -.0244674   .0130412    -1.88   0.064    -.0503925    .0014576
               2015  |  -.0280786   .0127681    -2.20   0.031    -.0534608   -.0026964
               2016  |  -.0284123   .0129324    -2.20   0.031    -.0541212   -.0027035
               2017  |  -.0260615   .0134724    -1.93   0.056    -.0528437    .0007207
               2018  |  -.0217001   .0139939    -1.55   0.125    -.0495191    .0061189
                     |
               _cons |   .0963126   .0117608     8.19   0.000      .072933    .1196923
---------------------+----------------------------------------------------------------
             sigma_u |  .03822203
             sigma_e |  .04785121
                 rho |  .38951092   (fraction of variance due to u_i)
--------------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_constitutional) ctitle(13) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_assembly i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0364                                         min =         10
     Between = 0.0113                                         avg =       26.9
     Overall = 0.0263                                         max =         29

                                                F(29, 86)         =       2.09
corr(u_i, Xb) = -0.0026                         Prob > F          =     0.0047

                              (Std. err. adjusted for 87 clusters in country_id)
--------------------------------------------------------------------------------
               |               Robust
natsubnatdif~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------+----------------------------------------------------------------
rai_n_assembly |   .0030364   .0080437     0.38   0.707     -.012954    .0190268
               |
          year |
         1991  |  -.0132871   .0117119    -1.13   0.260    -.0365696    .0099955
         1992  |   -.013412   .0113494    -1.18   0.241    -.0359738    .0091498
         1993  |  -.0152321   .0111755    -1.36   0.176    -.0374483    .0069841
         1994  |  -.0196342   .0115193    -1.70   0.092    -.0425338    .0032654
         1995  |  -.0184571   .0116812    -1.58   0.118    -.0416785    .0047643
         1996  |  -.0158903   .0118819    -1.34   0.185    -.0395108    .0077302
         1997  |   -.022512   .0116849    -1.93   0.057    -.0457408    .0007168
         1998  |  -.0197001   .0116147    -1.70   0.093    -.0427893    .0033891
         1999  |  -.0136373   .0115684    -1.18   0.242    -.0366345    .0093599
         2000  |  -.0089296   .0132427    -0.67   0.502    -.0352551    .0173959
         2001  |  -.0153479   .0124493    -1.23   0.221    -.0400964    .0094006
         2002  |  -.0201941   .0119472    -1.69   0.095    -.0439444    .0035561
         2003  |  -.0215346   .0123902    -1.74   0.086    -.0461654    .0030963
         2004  |   -.013868   .0127281    -1.09   0.279    -.0391707    .0114346
         2005  |  -.0147852   .0131628    -1.12   0.264    -.0409521    .0113816
         2006  |  -.0212383   .0128078    -1.66   0.101    -.0466993    .0042228
         2007  |   -.032481    .012433    -2.61   0.011     -.057197   -.0077649
         2008  |   -.036168   .0125316    -2.89   0.005    -.0610799   -.0112561
         2009  |  -.0410628   .0123753    -3.32   0.001    -.0656642   -.0164615
         2010  |  -.0385371   .0125176    -3.08   0.003    -.0634212   -.0136529
         2011  |  -.0349252   .0127834    -2.73   0.008    -.0603378   -.0095126
         2012  |  -.0337288   .0128727    -2.62   0.010    -.0593188   -.0081387
         2013  |  -.0273924   .0131379    -2.08   0.040    -.0535096   -.0012751
         2014  |  -.0251665   .0136425    -1.84   0.069    -.0522868    .0019538
         2015  |  -.0288133   .0134188    -2.15   0.035    -.0554889   -.0021377
         2016  |   -.029219   .0137162    -2.13   0.036     -.056486    -.001952
         2017  |  -.0268981   .0144174    -1.87   0.065    -.0555588    .0017627
         2018  |  -.0227823   .0150529    -1.51   0.134    -.0527065    .0071418
               |
         _cons |   .0900136    .013506     6.66   0.000     .0631646    .1168627
---------------+----------------------------------------------------------------
       sigma_u |  .03775863
       sigma_e |  .04784477
           rho |  .38378929   (fraction of variance due to u_i)
--------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_assembly) ctitle(14) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_executive i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0370                                         min =         10
     Between = 0.0021                                         avg =       26.9
     Overall = 0.0206                                         max =         29

                                                F(29, 86)         =       2.11
corr(u_i, Xb) = -0.0437                         Prob > F          =     0.0043

                               (Std. err. adjusted for 87 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
natsubnatdiff~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
rai_n_executive |    .004129   .0091056     0.45   0.651    -.0139724    .0222303
                |
           year |
          1991  |  -.0135289   .0117583    -1.15   0.253    -.0369036    .0098457
          1992  |  -.0135654   .0114318    -1.19   0.239    -.0362911    .0091603
          1993  |  -.0153274   .0112222    -1.37   0.176    -.0376364    .0069816
          1994  |  -.0197686   .0115093    -1.72   0.089    -.0426483    .0031111
          1995  |   -.018858   .0117615    -1.60   0.113    -.0422391    .0045232
          1996  |  -.0163611   .0119356    -1.37   0.174    -.0400882     .007366
          1997  |  -.0228368   .0115949    -1.97   0.052    -.0458866    .0002131
          1998  |  -.0199087   .0115574    -1.72   0.089     -.042884    .0030666
          1999  |  -.0140298   .0115597    -1.21   0.228    -.0370097    .0089501
          2000  |  -.0093281   .0131273    -0.71   0.479    -.0354244    .0167682
          2001  |  -.0157365   .0122432    -1.29   0.202    -.0400752    .0086023
          2002  |  -.0207342   .0117571    -1.76   0.081    -.0441066    .0026382
          2003  |  -.0220832   .0120775    -1.83   0.071    -.0460925    .0019262
          2004  |  -.0145128   .0125588    -1.16   0.251    -.0394787    .0104532
          2005  |  -.0155592   .0130825    -1.19   0.238    -.0415663     .010448
          2006  |  -.0219644   .0125859    -1.75   0.085    -.0469844    .0030556
          2007  |  -.0331511   .0121974    -2.72   0.008    -.0573986   -.0089035
          2008  |  -.0367901   .0125195    -2.94   0.004     -.061678   -.0119022
          2009  |  -.0416686   .0125104    -3.33   0.001    -.0665386   -.0167987
          2010  |  -.0390383   .0125007    -3.12   0.002    -.0638889   -.0141877
          2011  |  -.0355345   .0128279    -2.77   0.007    -.0610356   -.0100334
          2012  |  -.0343994   .0129976    -2.65   0.010    -.0602378   -.0085609
          2013  |   -.028088   .0132702    -2.12   0.037    -.0544683   -.0017077
          2014  |  -.0259008   .0137711    -1.88   0.063    -.0532769    .0014754
          2015  |  -.0295993   .0135159    -2.19   0.031     -.056468   -.0027305
          2016  |  -.0299867   .0138443    -2.17   0.033    -.0575083   -.0024651
          2017  |  -.0277148   .0144214    -1.92   0.058    -.0563835    .0009539
          2018  |  -.0235823   .0150036    -1.57   0.120    -.0534084    .0062438
                |
          _cons |   .0900855   .0133195     6.76   0.000     .0636072    .1165639
----------------+----------------------------------------------------------------
        sigma_u |  .03809255
        sigma_e |  .04782972
            rho |  .38811158   (fraction of variance due to u_i)
---------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_executive) ctitle(15) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. xtreg natsubnatdiff_abs rai_n_instdepth rai_n_policyautonomy rai_n_fiscalautonomy rai_n_borrowautonomy rai_n_representatio
> n_11 rai_n_lawmaking_12 rai_n_execcontrol  rai_n_fiscalcontrol rai_n_borrowcontrol rai_n_constitutional rai_n_assembly rai
> _n_executive i.year , fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0521                                         min =         10
     Between = 0.0089                                         avg =       26.9
     Overall = 0.0247                                         max =         29

                                                F(40, 86)         =       2.23
corr(u_i, Xb) = -0.2504                         Prob > F          =     0.0010

                                       (Std. err. adjusted for 87 clusters in country_id)
-----------------------------------------------------------------------------------------
                        |               Robust
      natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
        rai_n_instdepth |  -.0035072   .0102488    -0.34   0.733    -.0238811    .0168668
   rai_n_policyautonomy |   .0056756   .0068566     0.83   0.410    -.0079548    .0193061
   rai_n_fiscalautonomy |   .0130022   .0077868     1.67   0.099    -.0024774    .0284819
   rai_n_borrowautonomy |  -.0113432   .0069805    -1.62   0.108      -.02522    .0025336
rai_n_representation_11 |  -.0769248   .0590023    -1.30   0.196    -.1942175     .040368
     rai_n_lawmaking_12 |  -.0115696   .0096704    -1.20   0.235    -.0307937    .0076545
      rai_n_execcontrol |   .0108194   .0114459     0.95   0.347    -.0119344    .0335731
    rai_n_fiscalcontrol |  -.0098737   .0092182    -1.07   0.287    -.0281988    .0084515
    rai_n_borrowcontrol |  -.0066489   .0091182    -0.73   0.468    -.0247753    .0114775
   rai_n_constitutional |   .0001981   .0037844     0.05   0.958     -.007325    .0077213
         rai_n_assembly |   .0807751   .0614357     1.31   0.192     -.041355    .2029052
        rai_n_executive |   .0770996   .0600029     1.28   0.202    -.0421822    .1963814
                        |
                   year |
                  1991  |  -.0130482   .0118153    -1.10   0.273    -.0365363    .0104398
                  1992  |  -.0133745   .0114267    -1.17   0.245      -.03609    .0093411
                  1993  |  -.0160576   .0112493    -1.43   0.157    -.0384204    .0063052
                  1994  |  -.0200623   .0116697    -1.72   0.089    -.0432608    .0031363
                  1995  |  -.0189084   .0119041    -1.59   0.116     -.042573    .0047563
                  1996  |   -.016616   .0119985    -1.38   0.170    -.0404682    .0072362
                  1997  |  -.0236288   .0118231    -2.00   0.049    -.0471325   -.0001252
                  1998  |   -.021169   .0118744    -1.78   0.078    -.0447746    .0024366
                  1999  |  -.0149172    .011938    -1.25   0.215    -.0386492    .0088148
                  2000  |  -.0115571   .0137294    -0.84   0.402    -.0388503     .015736
                  2001  |  -.0184348   .0129532    -1.42   0.158     -.044185    .0073153
                  2002  |  -.0231924   .0125512    -1.85   0.068    -.0481435    .0017586
                  2003  |  -.0244127   .0128578    -1.90   0.061    -.0499732    .0011478
                  2004  |  -.0167862     .01342    -1.25   0.214    -.0434644     .009892
                  2005  |  -.0181994   .0139362    -1.31   0.195    -.0459036    .0095049
                  2006  |  -.0243182   .0134789    -1.80   0.075    -.0511134    .0024769
                  2007  |  -.0352709   .0131239    -2.69   0.009    -.0613603   -.0091815
                  2008  |  -.0387549   .0133981    -2.89   0.005    -.0653894   -.0121205
                  2009  |  -.0434711   .0134668    -3.23   0.002    -.0702422   -.0167001
                  2010  |   -.041275   .0135983    -3.04   0.003    -.0683076   -.0142425
                  2011  |  -.0375682   .0139736    -2.69   0.009    -.0653468   -.0097897
                  2012  |  -.0365644   .0138257    -2.64   0.010     -.064049   -.0090798
                  2013  |  -.0303492   .0138586    -2.19   0.031    -.0578992   -.0027991
                  2014  |  -.0282738   .0145058    -1.95   0.055    -.0571104    .0005627
                  2015  |  -.0317638   .0142928    -2.22   0.029    -.0601769   -.0033507
                  2016  |  -.0323532    .014714    -2.20   0.031    -.0616037   -.0031027
                  2017  |  -.0301927   .0153507    -1.97   0.052    -.0607089    .0003234
                  2018  |  -.0255963   .0159243    -1.61   0.112    -.0572527    .0060601
                        |
                  _cons |   .0912993   .0140318     6.51   0.000     .0634051    .1191936
------------------------+----------------------------------------------------------------
                sigma_u |  .03975362
                sigma_e |  .04757064
                    rho |  .41119451   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------

. outreg2 using mydoc2.doc, append keep(rai_n_instdepth rai_n_policyautonomy rai_n_fiscalautonomy rai_n_borrowautonomy rai_n
> _representation_11 rai_n_lawmaking_12 rai_n_execcontrol  rai_n_fiscalcontrol rai_n_borrowcontrol rai_n_constitutional rai_
> n_assembly rai_n_executive) ctitle(16) addtext(Country FE, Yes, Year FE, Yes) label
mydoc2.doc
dir : seeout

. *
. *REPEAT REGRESSIONS FOR FACTORS WITH SIGNIFICANCE AT SOME POINT. 
. *STORE THOSE ESTIMATES AND PLOT THEM
. *
. xtreg natsubnatdiff_abs inv_state_fsi i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,354
Group variable: country_id                      Number of groups  =        161

R-squared:                                      Obs per group:
     Within  = 0.0255                                         min =          1
     Between = 0.0631                                         avg =       14.6
     Overall = 0.0564                                         max =         17

                                                F(17, 160)        =       0.90
corr(u_i, Xb) = -0.1060                         Prob > F          =     0.5715

                            (Std. err. adjusted for 161 clusters in country_id)
-------------------------------------------------------------------------------
              |               Robust
natsubnatdi~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
inv_state_fsi |  -.0008861   .0006018    -1.47   0.143    -.0020745    .0003023
              |
         year |
        2007  |  -.0035202   .0039376    -0.89   0.373    -.0112966    .0042562
        2008  |  -.0024173   .0047363    -0.51   0.610     -.011771    .0069365
        2009  |  -.0048729   .0053454    -0.91   0.363    -.0154295    .0056837
        2010  |  -.0059032   .0062297    -0.95   0.345    -.0182063    .0063998
        2011  |  -.0020923    .006368    -0.33   0.743    -.0146685    .0104838
        2012  |   -.004624   .0063803    -0.72   0.470    -.0172245    .0079766
        2013  |   .0002449   .0061382     0.04   0.968    -.0118773    .0123672
        2014  |   .0029084     .00602     0.48   0.630    -.0089806    .0147974
        2015  |   .0024178   .0060542     0.40   0.690    -.0095387    .0143743
        2016  |   .0007466   .0061843     0.12   0.904    -.0114668      .01296
        2017  |    .004914   .0063129     0.78   0.437    -.0075533    .0173813
        2018  |   .0049309   .0067803     0.73   0.468    -.0084595    .0183213
        2019  |   .0187652   .0084873     2.21   0.028     .0020035    .0355269
        2020  |   .0200543   .0086724     2.31   0.022     .0029272    .0371814
        2021  |   .0169386   .0082922     2.04   0.043     .0005623    .0333149
        2022  |   .0157466   .0084325     1.87   0.064    -.0009067       .0324
              |
        _cons |   .1309505   .0358801     3.65   0.000     .0600908    .2018102
--------------+----------------------------------------------------------------
      sigma_u |  .05487685
      sigma_e |  .04798119
          rho |  .56674057   (fraction of variance due to u_i)
-------------------------------------------------------------------------------

. estimates store statecapBV

. *
. xtreg natsubnatdiff_abs rai_index  i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0361                                         min =         10
     Between = 0.0051                                         avg =       26.9
     Overall = 0.0237                                         max =         29

                                                F(29, 86)         =       2.12
corr(u_i, Xb) = -0.0188                         Prob > F          =     0.0040

                            (Std. err. adjusted for 87 clusters in country_id)
------------------------------------------------------------------------------
             |               Robust
natsubnatd~s | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   rai_index |   .0004067   .0010084     0.40   0.688    -.0015979    .0024114
             |
        year |
       1991  |  -.0133711   .0117389    -1.14   0.258    -.0367073     .009965
       1992  |   -.013453   .0113776    -1.18   0.240    -.0360709    .0091649
       1993  |   -.015138   .0111087    -1.36   0.177    -.0372213    .0069453
       1994  |  -.0195075    .011365    -1.72   0.090    -.0421004    .0030854
       1995  |  -.0183864   .0115466    -1.59   0.115    -.0413404    .0045675
       1996  |  -.0158289    .011708    -1.35   0.180    -.0391036    .0074457
       1997  |  -.0224036    .011442    -1.96   0.053    -.0451496    .0003424
       1998  |  -.0195515    .011388    -1.72   0.090    -.0421901    .0030871
       1999  |   -.013501   .0113363    -1.19   0.237    -.0360368    .0090348
       2000  |  -.0087361   .0128598    -0.68   0.499    -.0343004    .0168283
       2001  |  -.0151924   .0122015    -1.25   0.216    -.0394481    .0090633
       2002  |  -.0200076   .0117079    -1.71   0.091    -.0432821     .003267
       2003  |  -.0213451   .0120706    -1.77   0.081    -.0453406    .0026505
       2004  |  -.0137439   .0124522    -1.10   0.273     -.038498    .0110103
       2005  |  -.0146822   .0129519    -1.13   0.260    -.0404297    .0110653
       2006  |  -.0210962   .0124631    -1.69   0.094     -.045872    .0036796
       2007  |  -.0322819   .0120902    -2.67   0.009    -.0563165   -.0082473
       2008  |  -.0360227   .0122387    -2.94   0.004    -.0603525   -.0116929
       2009  |  -.0408783   .0121393    -3.37   0.001    -.0650104   -.0167462
       2010  |  -.0382988   .0122211    -3.13   0.002    -.0625935    -.014004
       2011  |  -.0347375   .0124975    -2.78   0.007    -.0595817   -.0098934
       2012  |  -.0335697    .012618    -2.66   0.009    -.0586534    -.008486
       2013  |  -.0271922    .012832    -2.12   0.037    -.0527015    -.001683
       2014  |  -.0249622   .0132668    -1.88   0.063    -.0513357    .0014113
       2015  |  -.0286866   .0130531    -2.20   0.031    -.0546353   -.0027379
       2016  |  -.0290693   .0134058    -2.17   0.033    -.0557191   -.0024195
       2017  |  -.0267428    .014053    -1.90   0.060    -.0546793    .0011937
       2018  |  -.0225697   .0146063    -1.55   0.126    -.0516062    .0064667
             |
       _cons |   .0903211   .0131892     6.85   0.000     .0641019    .1165404
-------------+----------------------------------------------------------------
     sigma_u |  .03792709
     sigma_e |  .04785176
         rho |  .38582772   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. estimates store raiBV

. *
. xtreg natsubnatdiff_abs emb_overall_capacity  i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =      2,792
Group variable: country_id                      Number of groups  =         94

R-squared:                                      Obs per group:
     Within  = 0.0333                                         min =          1
     Between = 0.1677                                         avg =       29.7
     Overall = 0.0965                                         max =         33

                                                Wald chi2(41)     =     118.39
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                         (Std. err. adjusted for 94 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
     emb_overall_capacity |    .004471   .0069933     0.64   0.523    -.0092357    .0181777
                          |
                region_id |
               East Asia  |  -.0227733    .010287    -2.21   0.027    -.0429355    -.002611
           Latin America  |  -.0120778    .010957    -1.10   0.270    -.0335531    .0093975
                    MENA  |   .0108948    .016504     0.66   0.509    -.0214525    .0432421
              South Asia  |   .0211476   .0197794     1.07   0.285    -.0176194    .0599146
         South-East Asia  |   .0291427   .0200758     1.45   0.147    -.0102052    .0684906
      Sub-Saharan Africa  |   .0440384   .0175147     2.51   0.012     .0097103    .0783665
             The Pacific  |   .0507638   .0547168     0.93   0.354    -.0564792    .1580067
W. Europe and N. America  |  -.0069783   .0110247    -0.63   0.527    -.0285864    .0146298
                          |
                     year |
                    1991  |  -.0153768    .009742    -1.58   0.114    -.0344707    .0037172
                    1992  |  -.0113945   .0132606    -0.86   0.390    -.0373848    .0145958
                    1993  |  -.0198229   .0123182    -1.61   0.108    -.0439661    .0043203
                    1994  |  -.0270835   .0131118    -2.07   0.039    -.0527822   -.0013847
                    1995  |  -.0344276   .0127912    -2.69   0.007     -.059498   -.0093573
                    1996  |  -.0341793   .0129252    -2.64   0.008    -.0595123   -.0088463
                    1997  |  -.0374346   .0129602    -2.89   0.004    -.0628361   -.0120331
                    1998  |  -.0355531   .0127936    -2.78   0.005    -.0606281   -.0104781
                    1999  |  -.0279051   .0127277    -2.19   0.028    -.0528509   -.0029593
                    2000  |  -.0277057   .0132875    -2.09   0.037    -.0537486   -.0016628
                    2001  |  -.0309377   .0134094    -2.31   0.021    -.0572197   -.0046558
                    2002  |   -.037495   .0130404    -2.88   0.004    -.0630538   -.0119362
                    2003  |   -.035013   .0135376    -2.59   0.010    -.0615463   -.0084797
                    2004  |  -.0352053   .0138941    -2.53   0.011    -.0624371   -.0079734
                    2005  |   -.035632   .0142414    -2.50   0.012    -.0635447   -.0077194
                    2006  |  -.0462253   .0138398    -3.34   0.001    -.0733509   -.0190998
                    2007  |  -.0456264   .0137436    -3.32   0.001    -.0725635   -.0186894
                    2008  |  -.0474391   .0138921    -3.41   0.001    -.0746671    -.020211
                    2009  |  -.0507721   .0136478    -3.72   0.000    -.0775213   -.0240228
                    2010  |   -.048702   .0143045    -3.40   0.001    -.0767383   -.0206657
                    2011  |  -.0486342   .0144448    -3.37   0.001    -.0769454   -.0203229
                    2012  |  -.0506155   .0145307    -3.48   0.000    -.0790952   -.0221359
                    2013  |  -.0450133   .0144765    -3.11   0.002    -.0733868   -.0166399
                    2014  |  -.0433942   .0144504    -3.00   0.003    -.0717165   -.0150719
                    2015  |  -.0416622   .0146923    -2.84   0.005    -.0704585   -.0128658
                    2016  |  -.0415676   .0151547    -2.74   0.006    -.0712702    -.011865
                    2017  |  -.0374062   .0155068    -2.41   0.016     -.067799   -.0070134
                    2018  |  -.0393407   .0154134    -2.55   0.011    -.0695504    -.009131
                    2019  |  -.0312077   .0155463    -2.01   0.045    -.0616779   -.0007376
                    2020  |  -.0312592   .0159409    -1.96   0.050    -.0625027   -.0000157
                    2021  |  -.0349898   .0152809    -2.29   0.022    -.0649399   -.0050397
                    2022  |  -.0349721   .0155979    -2.24   0.025    -.0655434   -.0044009
                          |
                    _cons |   .1051397   .0192779     5.45   0.000     .0673558    .1429236
--------------------------+----------------------------------------------------------------
                  sigma_u |  .04229735
                  sigma_e |  .05939583
                      rho |  .33648472   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. estimates store embcapBV

. *
. xtreg natsubnatdiff_abs i.emb_typeBIN  i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =      4,706
Group variable: country_id                      Number of groups  =        164

R-squared:                                      Obs per group:
     Within  = 0.0318                                         min =          1
     Between = 0.2026                                         avg =       28.7
     Overall = 0.1192                                         max =         33

                                                Wald chi2(42)     =     114.56
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                        (Std. err. adjusted for 164 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
              emb_typeBIN |
        Government/Mixed  |  -.0234075   .0085351    -2.74   0.006     -.040136   -.0066791
                          |
                region_id |
               East Asia  |  -.0146378   .0134312    -1.09   0.276    -.0409624    .0116868
           Latin America  |  -.0040441   .0086783    -0.47   0.641    -.0210532     .012965
                    MENA  |    .029876   .0130432     2.29   0.022     .0043117    .0554403
              South Asia  |   .0249021   .0168614     1.48   0.140    -.0081457    .0579498
         South-East Asia  |    .019425   .0135006     1.44   0.150    -.0070357    .0458857
      Sub-Saharan Africa  |   .0453943   .0116168     3.91   0.000     .0226257    .0681629
           The Carribean  |  -.0159777    .012473    -1.28   0.200    -.0404243    .0084689
             The Pacific  |   .0407259   .0297323     1.37   0.171    -.0175485    .0990002
W. Europe and N. America  |   .0043617   .0099155     0.44   0.660    -.0150724    .0237957
                          |
                     year |
                    1991  |  -.0125469   .0064256    -1.95   0.051    -.0251408    .0000471
                    1992  |  -.0076658   .0094106    -0.81   0.415    -.0261103    .0107788
                    1993  |  -.0210925   .0096019    -2.20   0.028    -.0399119   -.0022732
                    1994  |  -.0280459   .0096221    -2.91   0.004    -.0469049   -.0091868
                    1995  |  -.0314478   .0093608    -3.36   0.001    -.0497947   -.0131009
                    1996  |  -.0303046   .0095972    -3.16   0.002    -.0491147   -.0114944
                    1997  |  -.0345323   .0092966    -3.71   0.000    -.0527533   -.0163114
                    1998  |  -.0343591   .0093042    -3.69   0.000     -.052595   -.0161231
                    1999  |  -.0284642   .0094233    -3.02   0.003    -.0469336   -.0099948
                    2000  |  -.0260225   .0099043    -2.63   0.009    -.0454346   -.0066104
                    2001  |  -.0301092   .0099084    -3.04   0.002    -.0495293   -.0106892
                    2002  |  -.0341384    .009843    -3.47   0.001    -.0534303   -.0148466
                    2003  |  -.0347263   .0101284    -3.43   0.001    -.0545777    -.014875
                    2004  |   -.035347   .0102565    -3.45   0.001    -.0554494   -.0152446
                    2005  |  -.0372329   .0104647    -3.56   0.000    -.0577432   -.0167225
                    2006  |   -.040993   .0103736    -3.95   0.000     -.061325    -.020661
                    2007  |  -.0445161   .0103873    -4.29   0.000    -.0648749   -.0241573
                    2008  |  -.0442177   .0104686    -4.22   0.000    -.0647358   -.0236995
                    2009  |  -.0456555   .0102958    -4.43   0.000    -.0658349    -.025476
                    2010  |  -.0450834   .0105097    -4.29   0.000    -.0656819   -.0244849
                    2011  |  -.0433002   .0105463    -4.11   0.000    -.0639704   -.0226299
                    2012  |  -.0463606   .0105472    -4.40   0.000    -.0670327   -.0256885
                    2013  |  -.0420229   .0107204    -3.92   0.000    -.0630346   -.0210112
                    2014  |  -.0401841   .0108138    -3.72   0.000    -.0613787   -.0189896
                    2015  |  -.0407472    .010884    -3.74   0.000    -.0620794   -.0194149
                    2016  |  -.0423493   .0108646    -3.90   0.000    -.0636435    -.021055
                    2017  |  -.0389791   .0110171    -3.54   0.000    -.0605723   -.0173859
                    2018  |  -.0394131   .0111229    -3.54   0.000    -.0612136   -.0176126
                    2019  |  -.0274664   .0112481    -2.44   0.015    -.0495122   -.0054205
                    2020  |  -.0283142   .0111649    -2.54   0.011    -.0501971   -.0064314
                    2021  |  -.0288205    .011029    -2.61   0.009     -.050437   -.0072041
                    2022  |  -.0306713   .0111179    -2.76   0.006    -.0524621   -.0088806
                          |
                    _cons |   .1097093   .0108324    10.13   0.000     .0884783    .1309404
--------------------------+----------------------------------------------------------------
                  sigma_u |  .04226047
                  sigma_e |  .05768624
                      rho |  .34925098   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. estimates store embtyBV

. *
. xtreg natsubnatdiff_abs rai_n_fiscalautonomy i.year, fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0384                                         min =         10
     Between = 0.0069                                         avg =       26.9
     Overall = 0.0222                                         max =         29

                                                F(29, 86)         =       2.30
corr(u_i, Xb) = -0.1285                         Prob > F          =     0.0016

                                    (Std. err. adjusted for 87 clusters in country_id)
--------------------------------------------------------------------------------------
                     |               Robust
   natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
---------------------+----------------------------------------------------------------
rai_n_fiscalautonomy |   .0067351    .004527     1.49   0.140    -.0022643    .0157346
                     |
                year |
               1991  |  -.0132611    .011729    -1.13   0.261    -.0365776    .0100555
               1992  |  -.0132994   .0113415    -1.17   0.244    -.0358456    .0092469
               1993  |  -.0149737   .0110064    -1.36   0.177    -.0368538    .0069063
               1994  |  -.0194948   .0112008    -1.74   0.085    -.0417612    .0027717
               1995  |  -.0185177   .0113324    -1.63   0.106    -.0410458    .0040103
               1996  |  -.0158717    .011435    -1.39   0.169    -.0386038    .0068604
               1997  |  -.0225693   .0111789    -2.02   0.047    -.0447921   -.0003465
               1998  |  -.0198922   .0111755    -1.78   0.079    -.0421083     .002324
               1999  |  -.0137926    .011087    -1.24   0.217    -.0358329    .0082477
               2000  |  -.0091898   .0124281    -0.74   0.462    -.0338961    .0155164
               2001  |  -.0157799   .0119857    -1.32   0.191    -.0396066    .0080469
               2002  |  -.0204788   .0115356    -1.78   0.079    -.0434108    .0024533
               2003  |  -.0218882   .0118217    -1.85   0.068     -.045389    .0016126
               2004  |   -.014303   .0122432    -1.17   0.246    -.0386416    .0100356
               2005  |   -.015287   .0126922    -1.20   0.232    -.0405183    .0099444
               2006  |  -.0215163   .0121814    -1.77   0.081    -.0457321    .0026994
               2007  |  -.0325113    .011843    -2.75   0.007    -.0560544   -.0089682
               2008  |  -.0362278   .0118718    -3.05   0.003    -.0598281   -.0126274
               2009  |  -.0409728   .0118137    -3.47   0.001    -.0644577   -.0174879
               2010  |  -.0385949   .0118999    -3.24   0.002    -.0622511   -.0149387
               2011  |  -.0349782   .0121238    -2.89   0.005    -.0590794   -.0108769
               2012  |  -.0338402    .012342    -2.74   0.007    -.0583753    -.009305
               2013  |  -.0274859   .0126112    -2.18   0.032    -.0525562   -.0024156
               2014  |  -.0253088   .0128959    -1.96   0.053     -.050945    .0003275
               2015  |  -.0289414   .0126385    -2.29   0.024     -.054066   -.0038168
               2016  |  -.0293283   .0128733    -2.28   0.025    -.0549197    -.003737
               2017  |  -.0269872   .0134345    -2.01   0.048    -.0536942   -.0002802
               2018  |  -.0225934   .0139731    -1.62   0.110    -.0503709    .0051842
                     |
               _cons |   .0875083   .0115193     7.60   0.000     .0646087     .110408
---------------------+----------------------------------------------------------------
             sigma_u |  .03854732
             sigma_e |  .04779421
                 rho |  .39411775   (fraction of variance due to u_i)
--------------------------------------------------------------------------------------

. estimates store fiscauBV

. *
. xtreg natsubnatdiff_abs gdppc_log e_wb_pop_log ethnic_frac terrain_rugged i.FEDERALISM_DUMMY inv_state_fsi rai_index emb_o
> verall_capacity i.emb_typeBIN i.pres_parlBIN i.prop_rep_bin i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =        293
Group variable: country_id                      Number of groups  =         50

R-squared:                                      Obs per group:
     Within  = 0.1393                                         min =          4
     Between = 0.4438                                         avg =        5.9
     Overall = 0.3200                                         max =          6

                                                Wald chi2(22)     =     140.38
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                         (Std. err. adjusted for 50 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
                gdppc_log |  -.0223206   .0199921    -1.12   0.264    -.0615044    .0168632
             e_wb_pop_log |   .0045417   .0083679     0.54   0.587    -.0118591    .0209425
              ethnic_frac |  -.0289433   .0449412    -0.64   0.520    -.1170265    .0591399
           terrain_rugged |   .0000745   .0000698     1.07   0.286    -.0000623    .0002114
       1.FEDERALISM_DUMMY |  -.0259158   .0223306    -1.16   0.246     -.069683    .0178515
            inv_state_fsi |   .0014004   .0003796     3.69   0.000     .0006565    .0021444
                rai_index |   .0015486   .0021945     0.71   0.480    -.0027524    .0058497
     emb_overall_capacity |  -.0551842   .0167992    -3.28   0.001      -.08811   -.0222584
                          |
              emb_typeBIN |
        Government/Mixed  |  -.0223201   .0182584    -1.22   0.222    -.0581059    .0134658
           1.pres_parlBIN |   .0437761   .0344657     1.27   0.204    -.0237755    .1113277
           1.prop_rep_bin |   .0060435   .0089591     0.67   0.500    -.0115159     .023603
                          |
                region_id |
               East Asia  |  -.0741803   .0393576    -1.88   0.059    -.1513198    .0029592
           Latin America  |  -.0435205   .0265001    -1.64   0.101    -.0954598    .0084189
                    MENA  |  -.0856193   .0350635    -2.44   0.015    -.1543426    -.016896
              South Asia  |   .0529202   .0409126     1.29   0.196     -.027267    .1331073
         South-East Asia  |   .0080508   .0350763     0.23   0.818    -.0606975     .076799
W. Europe and N. America  |  -.0306519     .02606    -1.18   0.240    -.0817286    .0204247
                          |
                     year |
                    2007  |  -.0051973   .0054464    -0.95   0.340    -.0158722    .0054775
                    2008  |  -.0075527   .0070757    -1.07   0.286    -.0214208    .0063154
                    2009  |  -.0147701    .008159    -1.81   0.070    -.0307615    .0012212
                    2010  |  -.0133557   .0106459    -1.25   0.210    -.0342213      .00751
                    2011  |  -.0107638    .011273    -0.95   0.340    -.0328584    .0113309
                          |
                    _cons |   .2395634   .1792369     1.34   0.181    -.1117345    .5908613
--------------------------+----------------------------------------------------------------
                  sigma_u |  .03604585
                  sigma_e |  .03193609
                      rho |  .56023361   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. estimates store MVOne

. *
. xtreg natsubnatdiff_abs gdppc_log e_wb_pop_log ethnic_frac terrain_rugged i.FEDERALISM_DUMMY inv_state_sfi rai_index emb_o
> verall_capacity i.emb_typeBIN i.pres_parlBIN i.prop_rep_bin i.region_id i.year, robust cl(country_id)

Random-effects GLS regression                   Number of obs     =        832
Group variable: country_id                      Number of groups  =         51

R-squared:                                      Obs per group:
     Within  = 0.1117                                         min =          4
     Between = 0.2559                                         avg =       16.3
     Overall = 0.1917                                         max =         17

                                                Wald chi2(33)     =     200.65
corr(u_i, X) = 0 (assumed)                      Prob > chi2       =     0.0000

                                         (Std. err. adjusted for 51 clusters in country_id)
-------------------------------------------------------------------------------------------
                          |               Robust
        natsubnatdiff_abs | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
                gdppc_log |  -.0133828   .0212066    -0.63   0.528    -.0549469    .0281813
             e_wb_pop_log |   .0004406   .0084892     0.05   0.959    -.0161978    .0170791
              ethnic_frac |  -.0060386   .0331773    -0.18   0.856    -.0710649    .0589878
           terrain_rugged |    .000111   .0000764     1.45   0.146    -.0000387    .0002607
       1.FEDERALISM_DUMMY |  -.0335433   .0220748    -1.52   0.129    -.0768092    .0097226
            inv_state_sfi |   .0023859   .0027415     0.87   0.384    -.0029873    .0077591
                rai_index |   .0030544   .0016632     1.84   0.066    -.0002055    .0063142
     emb_overall_capacity |  -.0229029   .0167207    -1.37   0.171    -.0556749    .0098692
                          |
              emb_typeBIN |
        Government/Mixed  |  -.0161226   .0180216    -0.89   0.371    -.0514442    .0191991
           1.pres_parlBIN |    .029306   .0294797     0.99   0.320    -.0284731     .087085
           1.prop_rep_bin |  -.0392869   .0242566    -1.62   0.105     -.086829    .0082552
                          |
                region_id |
               East Asia  |  -.0889691   .0403164    -2.21   0.027    -.1679877   -.0099504
           Latin America  |  -.0414354   .0270789    -1.53   0.126     -.094509    .0116383
                    MENA  |  -.0401355   .0332295    -1.21   0.227    -.1052641    .0249931
              South Asia  |   .0043354   .0469698     0.09   0.926    -.0877236    .0963945
         South-East Asia  |  -.0044191   .0374487    -0.12   0.906    -.0778171     .068979
W. Europe and N. America  |  -.0190109    .028963    -0.66   0.512    -.0757773    .0377556
                          |
                     year |
                    1996  |   .0038782    .004088     0.95   0.343    -.0041341    .0118906
                    1997  |   .0003067   .0037286     0.08   0.934    -.0070012    .0076147
                    1998  |   .0015712   .0069892     0.22   0.822    -.0121274    .0152697
                    1999  |   .0090515   .0069334     1.31   0.192    -.0045376    .0226407
                    2000  |    .013352   .0082425     1.62   0.105    -.0028031    .0295071
                    2001  |   .0078904   .0092419     0.85   0.393    -.0102234    .0260042
                    2002  |  -.0031161   .0099677    -0.31   0.755    -.0226525    .0164203
                    2003  |  -.0012145   .0100612    -0.12   0.904    -.0209342    .0185052
                    2004  |   .0098767   .0138381     0.71   0.475    -.0172454    .0369988
                    2005  |   .0101985   .0153257     0.67   0.506    -.0198394    .0402363
                    2006  |   .0009851   .0154737     0.06   0.949    -.0293427     .031313
                    2007  |   -.005923   .0161526    -0.37   0.714    -.0375815    .0257355
                    2008  |  -.0108904   .0171279    -0.64   0.525    -.0444604    .0226796
                    2009  |  -.0171031   .0173764    -0.98   0.325    -.0511602     .016954
                    2010  |  -.0153879   .0191732    -0.80   0.422    -.0529668    .0221909
                    2011  |  -.0118083   .0207281    -0.57   0.569    -.0524346    .0288179
                          |
                    _cons |   .2000453   .1720748     1.16   0.245    -.1372152    .5373057
--------------------------+----------------------------------------------------------------
                  sigma_u |  .04144506
                  sigma_e |  .04275107
                      rho |  .48449221   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------------

. estimates store MVTwo

. *
. xtreg natsubnatdiff_abs rai_n_instdepth rai_n_policyautonomy rai_n_fiscalautonomy rai_n_borrowautonomy rai_n_representatio
> n_11 rai_n_lawmaking_12 rai_n_execcontrol  rai_n_fiscalcontrol rai_n_borrowcontrol rai_n_constitutional rai_n_assembly rai
> _n_executive i.year , fe robust cl(country_id)

Fixed-effects (within) regression               Number of obs     =      2,338
Group variable: country_id                      Number of groups  =         87

R-squared:                                      Obs per group:
     Within  = 0.0521                                         min =         10
     Between = 0.0089                                         avg =       26.9
     Overall = 0.0247                                         max =         29

                                                F(40, 86)         =       2.23
corr(u_i, Xb) = -0.2504                         Prob > F          =     0.0010

                                       (Std. err. adjusted for 87 clusters in country_id)
-----------------------------------------------------------------------------------------
                        |               Robust
      natsubnatdiff_abs | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------+----------------------------------------------------------------
        rai_n_instdepth |  -.0035072   .0102488    -0.34   0.733    -.0238811    .0168668
   rai_n_policyautonomy |   .0056756   .0068566     0.83   0.410    -.0079548    .0193061
   rai_n_fiscalautonomy |   .0130022   .0077868     1.67   0.099    -.0024774    .0284819
   rai_n_borrowautonomy |  -.0113432   .0069805    -1.62   0.108      -.02522    .0025336
rai_n_representation_11 |  -.0769248   .0590023    -1.30   0.196    -.1942175     .040368
     rai_n_lawmaking_12 |  -.0115696   .0096704    -1.20   0.235    -.0307937    .0076545
      rai_n_execcontrol |   .0108194   .0114459     0.95   0.347    -.0119344    .0335731
    rai_n_fiscalcontrol |  -.0098737   .0092182    -1.07   0.287    -.0281988    .0084515
    rai_n_borrowcontrol |  -.0066489   .0091182    -0.73   0.468    -.0247753    .0114775
   rai_n_constitutional |   .0001981   .0037844     0.05   0.958     -.007325    .0077213
         rai_n_assembly |   .0807751   .0614357     1.31   0.192     -.041355    .2029052
        rai_n_executive |   .0770996   .0600029     1.28   0.202    -.0421822    .1963814
                        |
                   year |
                  1991  |  -.0130482   .0118153    -1.10   0.273    -.0365363    .0104398
                  1992  |  -.0133745   .0114267    -1.17   0.245      -.03609    .0093411
                  1993  |  -.0160576   .0112493    -1.43   0.157    -.0384204    .0063052
                  1994  |  -.0200623   .0116697    -1.72   0.089    -.0432608    .0031363
                  1995  |  -.0189084   .0119041    -1.59   0.116     -.042573    .0047563
                  1996  |   -.016616   .0119985    -1.38   0.170    -.0404682    .0072362
                  1997  |  -.0236288   .0118231    -2.00   0.049    -.0471325   -.0001252
                  1998  |   -.021169   .0118744    -1.78   0.078    -.0447746    .0024366
                  1999  |  -.0149172    .011938    -1.25   0.215    -.0386492    .0088148
                  2000  |  -.0115571   .0137294    -0.84   0.402    -.0388503     .015736
                  2001  |  -.0184348   .0129532    -1.42   0.158     -.044185    .0073153
                  2002  |  -.0231924   .0125512    -1.85   0.068    -.0481435    .0017586
                  2003  |  -.0244127   .0128578    -1.90   0.061    -.0499732    .0011478
                  2004  |  -.0167862     .01342    -1.25   0.214    -.0434644     .009892
                  2005  |  -.0181994   .0139362    -1.31   0.195    -.0459036    .0095049
                  2006  |  -.0243182   .0134789    -1.80   0.075    -.0511134    .0024769
                  2007  |  -.0352709   .0131239    -2.69   0.009    -.0613603   -.0091815
                  2008  |  -.0387549   .0133981    -2.89   0.005    -.0653894   -.0121205
                  2009  |  -.0434711   .0134668    -3.23   0.002    -.0702422   -.0167001
                  2010  |   -.041275   .0135983    -3.04   0.003    -.0683076   -.0142425
                  2011  |  -.0375682   .0139736    -2.69   0.009    -.0653468   -.0097897
                  2012  |  -.0365644   .0138257    -2.64   0.010     -.064049   -.0090798
                  2013  |  -.0303492   .0138586    -2.19   0.031    -.0578992   -.0027991
                  2014  |  -.0282738   .0145058    -1.95   0.055    -.0571104    .0005627
                  2015  |  -.0317638   .0142928    -2.22   0.029    -.0601769   -.0033507
                  2016  |  -.0323532    .014714    -2.20   0.031    -.0616037   -.0031027
                  2017  |  -.0301927   .0153507    -1.97   0.052    -.0607089    .0003234
                  2018  |  -.0255963   .0159243    -1.61   0.112    -.0572527    .0060601
                        |
                  _cons |   .0912993   .0140318     6.51   0.000     .0634051    .1191936
------------------------+----------------------------------------------------------------
                sigma_u |  .03975362
                sigma_e |  .04757064
                    rho |  .41119451   (fraction of variance due to u_i)
-----------------------------------------------------------------------------------------

. estimates store MVRai

. *
. *COEF PLOT
. coefplot (statecapBV raiBV embcapBV embtyBV fiscauBV, label(Bivariate) ci /// 
>                 keep(inv_state_fsi rai_index emb_overall_capacity *.emb_typeBIN rai_n_fiscalautonomy) msymbol(o) msize(sma
> ll) mcolor(gs7)) ///
>                 (MVOne, label(Multivar. Model A) keep(inv_state_fsi emb_overall_capacity ) offset(0.25) msymbol(s) msize(s
> mall) mcolor(gs7)) /// 
>                 (MVTwo, label(Multivar. Model B) keep(rai_index *.emb_typeBIN) offset(0.25) msymbol(d) msize(small) mcolor
> (gs7)) /// 
>                 (MVRai, label(Multivar. Model C) keep(rai_n_fiscalautonomy) offset(0.25) msymbol(t) msize(small) mcolor(gs
> 7)) /// 
>                 , order (rai_index rai_n_fiscalautonomy emb_overall_capacity *.emb_typeBIN inv_state_fsi) /// 
>                 drop(_cons) xline(0, lcol(red) lpat(dash) lwidth(vthin)) levels(88) ciopts(lcolor(gs13)) ///
>                 byopts(xrescale) mlabel format(%9.3f) mlabposition(12) mlabgap(*2) mlabsize(vsmall) ///
>                 scheme(lean1) leg(pos(8) ring(0) size(small))  /// 
>                 xtitle("{&beta}") /// 
>                 xlabel(,labsize(small)) yscale(off) ysize(1) xsize(1) ///
>                 text(.85 -0.025 "Regional Authority Index", size(small) color(navy)) ///
>                 text(1.8 -0.025 "Regional Fiscal Autonomy", size(small) color(navy)) /// 
>                 text(3 -0.03 "EMB Capacity", size(small) color(navy)) /// 
>                 text(3.9 -0.06 "EMB Type (Gov.)", size(small) color(navy)) ///
>                 text(4.8 -0.022 "Fragile State Index", size(small) color(navy)) /// 
>                 text(0.7 -0.075 "Panel C")

. 
end of do-file

. use "/Users/javier/Desktop/V-Dem 2023/V-Dem-CY-Full+Others-v13.dta",clear
(V-Dem CY-Full+Others)

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *GEN POSITIVE VALUES
. gen v2elffelr_pos=v2elffelr+3.459
(9,461 missing values generated)

. gen v2elsnlsff_pos=v2elsnlsff+3.219
(4,143 missing values generated)

. gen v2elfrfair_pos=v2elfrfair+3.373
(12,057 missing values generated)

. *STD BETWEEN 0 & 1
. foreach var in v2elffelr_pos v2xel_frefair v2elsnlsff_pos v2elfrfair_pos {
  2.         qui sum `var'
  3.         gen `var'_standard= (`var' - `r(min)') / (`r(max)'-`r(min)')
  4. }
(9,461 missing values generated)
(122 missing values generated)
(4,143 missing values generated)
(12,057 missing values generated)

. *
. xtset country_id year

Panel variable: country_id (unbalanced)
 Time variable: year, 1789 to 2022, but with gaps
         Delta: 1 unit

. gen ma_v2elffelr_pos_standard=(F1.v2elffelr_pos_standard+v2elffelr_pos_standard+L1.v2elffelr_pos_standard)/3
(10,138 missing values generated)

. *
. *ITALY
. tsline v2elfrfair_pos_standard if country_id==82 & year>2009, lcol(navy) yaxis(1) || /// 
> ,scheme(lean1) subtitle("National Free & Fair Elections", ring(0) pos(10) size(medsmall)) xtitle("Year",size(medsmall)) //
> / 
> ytitle("Score", axis(1) size(medsmall)) yscale(titlegap(0)) yscale(titlegap(0)) /// 
> ysize(1) xsize(1) xlabel(2010(2)2022) ylabel(,labsize(small) axis(1)) xlabel(,labsize(small))

. *
. tsline ma_v2elffelr_pos_standard  if country_id==82 & year>2009, lcol(purple) lpat(dash) yaxis(1) || /// 
> ,scheme(lean1) subtitle("Subational Free & Fair Elections", ring(0) pos(10) size(medsmall)) xtitle("Year",size(medsmall)) 
> /// 
> ytitle("Score", axis(1) size(medsmall)) yscale(titlegap(0)) yscale(titlegap(0)) /// 
> ysize(1) xsize(1) xlabel(2010(2)2022) ylabel(,labsize(small) axis(1)) xlabel(,labsize(small))

. *INDIA
. tsline v2elfrfair_pos_standard if country_id==39 & year>2009, lcol(navy) yaxis(1) || /// 
> ,scheme(lean1) subtitle("National Free & Fair Elections", ring(0) pos(10) size(medsmall)) xtitle("Year",size(medsmall)) //
> / 
> ytitle("Score", axis(1) size(medsmall)) yscale(titlegap(0)) yscale(titlegap(0)) /// 
> ysize(1) xsize(1) xlabel(2010(2)2022) ylabel(,labsize(small) axis(1)) xlabel(,labsize(small))

. *
. tsline ma_v2elffelr_pos_standard  if country_id==39 & year>2009, lcol(purple) lpat(dash) yaxis(1) || /// 
> ,scheme(lean1) subtitle("Subational Free & Fair Elections", ring(0) pos(10) size(medsmall)) xtitle("Year",size(medsmall)) 
> /// 
> ytitle("Score", axis(1) size(medsmall)) yscale(titlegap(0)) yscale(titlegap(0)) /// 
> ysize(1) xsize(1) xlabel(2010(2)2022) ylabel(,labsize(small) axis(1)) xlabel(,labsize(small))

. *
. *SOUTH AFRICA*
. tsline v2elfrfair_pos_standard if country_id==8 & year>2009, lcol(navy) yaxis(1) || /// 
> ,scheme(lean1) subtitle("National Free & Fair Elections", ring(0) pos(10) size(medsmall)) xtitle("Year",size(medsmall)) //
> / 
> ytitle("Score", axis(1) size(medsmall)) yscale(titlegap(0)) yscale(titlegap(0)) /// 
> ysize(1) xsize(1) xlabel(2010(2)2022) ylabel(,labsize(small) axis(1)) xlabel(,labsize(small))

. *
. tsline ma_v2elffelr_pos_standard  if country_id==8 & year>2009, lcol(purple) lpat(dash) yaxis(1) || /// 
> ,scheme(lean1) subtitle("Subational Free & Fair Elections", ring(0) pos(10) size(medsmall)) xtitle("Year",size(medsmall)) 
> /// 
> ytitle("Score", axis(1) size(medsmall)) yscale(titlegap(0)) yscale(titlegap(0)) /// 
> ysize(1) xsize(1) xlabel(2010(2)2022) ylabel(,labsize(small) axis(1)) xlabel(,labsize(small))

. *
. *UNITED STATES*
. tsline v2elfrfair_pos_standard if country_id==20 & year>2009, lcol(navy) yaxis(1) || /// 
> ,scheme(lean1) subtitle("National Free & Fair Elections", ring(0) pos(10) size(medsmall)) xtitle("Year",size(medsmall)) //
> / 
> ytitle("Score", axis(1) size(medsmall)) yscale(titlegap(0)) yscale(titlegap(0)) /// 
> ysize(1) xsize(1) xlabel(2010(2)2022) ylabel(,labsize(small) axis(1)) xlabel(,labsize(small))

. *
. tsline ma_v2elffelr_pos_standard  if country_id==20 & year>2009, lcol(purple) lpat(dash) yaxis(1) || /// 
> ,scheme(lean1) subtitle("Subational Free & Fair Elections", ring(0) pos(10) size(medsmall)) xtitle("Year",size(medsmall)) 
> /// 
> ytitle("Score", axis(1) size(medsmall)) yscale(titlegap(0)) yscale(titlegap(0)) /// 
> ysize(1) xsize(1) xlabel(2010(2)2022) ylabel(,labsize(small) axis(1)) xlabel(,labsize(small))

. 
end of do-file

. use "/Users/javier/Desktop/V-Dem 2023/V-Dem-CY-Full+Others-v13.dta",clear
(V-Dem CY-Full+Others)

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *
. *STANDARDIZE VARIABLES OF INTEREST
. *GEN POSITIVE VALUES
. gen v2elffelr_pos=v2elffelr+3.459
(9,461 missing values generated)

. gen v2elsnlsff_pos=v2elsnlsff+3.219
(4,143 missing values generated)

. gen v2elfrfair_pos=v2elfrfair+3.373
(12,057 missing values generated)

. *STD BETWEEN 0 & 1
. foreach var in v2elffelr_pos v2xel_frefair v2elsnlsff_pos v2elfrfair_pos {
  2.         qui sum `var'
  3.         gen `var'_standard= (`var' - `r(min)') / (`r(max)'-`r(min)')
  4. }
(9,461 missing values generated)
(122 missing values generated)
(4,143 missing values generated)
(12,057 missing values generated)

. *CHECK
. sum v2xel_frefair_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2xel_fre~rd |     27,433    .2791193    .3392966          0          1

. sum v2elffelr_pos_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2elffel~ard |     18,094    .5536238    .2421002          0          1

. sum v2elsnlsff_pos_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2elsnls~ard |     23,412    .6127363    .2320111          0          1

. sum v2elfrfair_pos_standard

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
v2elfrfa~ard |     15,498    .5430471    .2378209          0          1

. *
. *IDENTIFY PERIOD OF INTEREST
. gen period=. 
(27,555 missing values generated)

. replace period=1 if year>1989 & year<2000
(1,747 real changes made)

. replace period=2 if year>1999 & year<2010 
(1,773 real changes made)

. replace period=3 if year>2009 
(2,326 real changes made)

. replace period=-99 if period==.
(21,709 real changes made)

. *CONFIRM YEARS
. bys period: sum year

----------------------------------------------------------------------------------------------------------------------------
-> period = -99

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |     21,709    1907.444    56.44879       1789       1989

----------------------------------------------------------------------------------------------------------------------------
-> period = 1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |      1,747     1994.52    2.871636       1990       1999

----------------------------------------------------------------------------------------------------------------------------
-> period = 2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |      1,773    2004.506     2.87446       2000       2009

----------------------------------------------------------------------------------------------------------------------------
-> period = 3

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |      2,326    2016.003    3.741197       2010       2022


. *ESTIMATE AVERAGE DEM SCORE PER PERIOD
. gen e_polity2mod=e_polity2
(10,399 missing values generated)

. replace e_polity2mod=. if e_polity2<-10
(5 real changes made, 5 to missing)

. bys country_id period: egen mean_polity_period=mean(e_polity2mod)
(2,693 missing values generated)

. *IDENTIFY DEMOCRATIC REGIMES PER PERIOD. 
. gen dem_period_dummy=. 
(27,555 missing values generated)

. replace dem_period_dummy=1 if mean_polity_period>5
(8,180 real changes made)

. replace dem_period_dummy=0 if dem_period_dummy==. 
(19,375 real changes made)

. *
. keep country_id year e_regionpol v2elffelr_pos_standard v2xel_frefair_standard v2elsnlsff_pos_standard v2elfrfair_pos_stan
> dard period dem_period_dummy 

. *
. keep if year>1989
(21,709 observations deleted)

. *
. bysort country_id period (year): gen dem_nat_ave = (v2elfrfair_pos_standard + v2elfrfair_pos_standard[_n-1])/2 if year == 
> year[_n-1] + 1
(1,061 missing values generated)

. bysort country_id period (year): gen dem_subnat_ave = (v2elffelr_pos_standard + v2elffelr_pos_standard[_n-1])/2 if year ==
>  year[_n-1] + 1
(1,205 missing values generated)

. *
. bysort country_id period: gen first_nat_dem=dem_nat_ave if _n==2
(5,387 missing values generated)

. bysort country_id period: gen second_nat_dem=dem_nat_ave if _n==_N
(5,360 missing values generated)

. *
. bysort country_id period: gen first_subnat_dem=dem_subnat_ave if _n==2
(5,389 missing values generated)

. bysort country_id period: gen second_subnat_dem=dem_subnat_ave if _n==_N
(5,376 missing values generated)

. *
. bysort country_id period: egen mean_first_nat_dem=mean(first_nat_dem)
(763 missing values generated)

. bysort country_id period: egen mean_second_nat_dem=mean(second_nat_dem)
(509 missing values generated)

. *
. bysort country_id period: egen mean_first_subnat_dem=mean(first_subnat_dem)
(820 missing values generated)

. bysort country_id period: egen mean_second_subnat_dem=mean(second_subnat_dem)
(692 missing values generated)

. *
. *GENERATE  DIFFERENCES
. gen nat_difference=mean_second_nat_dem-mean_first_nat_dem
(957 missing values generated)

. gen subnat_difference=mean_second_subnat_dem-mean_first_subnat_dem
(1,010 missing values generated)

. *
. *GEN CLASSIFICATION OF QUADRANTS
. gen class_difflevel=. 
(5,846 missing values generated)

. replace class_difflevel=1 if nat_difference>=0  & subnat_difference>=0
(3,178 real changes made)

. replace class_difflevel=2 if nat_difference<0  & subnat_difference>=0
(1,389 real changes made)

. replace class_difflevel=3 if nat_difference<0  & subnat_difference<0
(832 real changes made)

. replace class_difflevel=4 if nat_difference>0  & subnat_difference<0
(447 real changes made)

. *
. egen tag=tag(country_id period class_difflevel)

. keep if tag==1
(5,310 observations deleted)

. **************************************************************************************************************************
> ************************
. **************************************************************************************************************************
> ************************
. *SECTION A GRAPHS: DUPLICATE THOSE IN THE PAPER
. *SECTION B GRAPHS
. /*1990-2000*/
. scatter subnat_difference nat_difference if period==1 & dem_period_dummy==1 /// 
> ,yline(0, lpat(dash) lcol(gs7)) xline(0, lpat(dash) lcol(gs7)) jitter(2) graphregion(margin(2 2 2 2)) plotregion(margin(0 
> 0 0 0)) yscale(titlegap(0)) ysize(1) xsize(1)  ylabel(-.6(.2).8) xlabel(-.6(.2).8) subtitle("1990-2000", pos(12)) xtitle("
> Nat. Free & Fair {&Delta} ") ytitle("Subnat. Free & Fair {&Delta} ") msymbol(circle_hollow) mlw(vthin) mlcol(black)  msize
> (small) scheme(lean1) /// 
> text(.7 .7 "I" .7 -.5 "II" -.5 .7 "IV" -.5 -.5 "III") /// 
> text(.6 .7 "73%" .6 -.5 "22%" -.4 .7 "1%" -.4 -.5 "4%", size(small)) 

. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. scatter subnat_difference nat_difference if period==2 & dem_period_dummy==1 /// 
> ,yline(0, lpat(dash) lcol(gs7)) xline(0, lpat(dash) lcol(gs7)) jitter(2)  graphregion(margin(2 2 2 2)) plotregion(margin(0
>  0 0 0)) yscale(titlegap(0)) ysize(1) xsize(1)  ylabel(-.6(.2).8) xlabel(-.6(.2).8) subtitle("2000-2010", pos(12)) xtitle(
> "Nat. Free & Fair {&Delta} ") ytitle("Subnat. Free & Fair {&Delta} ") msymbol(circle_hollow) mlw(vthin) mlcol(black)  msiz
> e(small) scheme(lean1) /// 
> text(.7 .7 "I" .7 -.5 "II" -.5 .7 "IV" -.5 -.5 "III") /// 
> text(.6 .7 "70%" .6 -.5 "18%" -.4 .7 "3%" -.4 -.5 "9%", size(small)) 

. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. scatter subnat_difference nat_difference if period==3 & dem_period_dummy==1  /// 
> ,yline(0, lpat(dash) lcol(gs7)) xline(0, lpat(dash) lcol(gs7)) jitter(2)  graphregion(margin(2 2 2 2)) plotregion(margin(0
>  0 0 0)) yscale(titlegap(0)) ysize(1) xsize(1)  ylabel(-.6(.2).8) xlabel(-.6(.2).8) subtitle("2010-2022", pos(12)) xtitle(
> "Nat. Free & Fair {&Delta} ") ytitle("Subnat. Free & Fair {&Delta} ") msymbol(circle_hollow) mlw(vthin) mlcol(black)  msiz
> e(small) scheme(lean1)  /// 
> text(.7 .7 "I" .7 -.5 "II" -.5 .7 "IV" -.5 -.5 "III") /// 
> text(.6 .7 "26%" .6 -.5 "41%" -.4 .7 "10%" -.4 -.5 "23%", size(small)) 

. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *
. *
. *TABULATE ALL AND DEMOCRACIES ONLY
. tab period class_difflevel, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |               class_difflevel
    period |         1          2          3          4 |     Total
-----------+--------------------------------------------+----------
         1 |       133         30         11          5 |       179 
           |     74.30      16.76       6.15       2.79 |    100.00 
-----------+--------------------------------------------+----------
         2 |       113         40         15         10 |       178 
           |     63.48      22.47       8.43       5.62 |    100.00 
-----------+--------------------------------------------+----------
         3 |        59         53         44         23 |       179 
           |     32.96      29.61      24.58      12.85 |    100.00 
-----------+--------------------------------------------+----------
     Total |       305        123         70         38 |       536 
           |     56.90      22.95      13.06       7.09 |    100.00 

. tab period class_difflevel if dem_period_dummy==1, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |               class_difflevel
    period |         1          2          3          4 |     Total
-----------+--------------------------------------------+----------
         1 |        64         19          4          1 |        88 
           |     72.73      21.59       4.55       1.14 |    100.00 
-----------+--------------------------------------------+----------
         2 |        72         18          9          3 |       102 
           |     70.59      17.65       8.82       2.94 |    100.00 
-----------+--------------------------------------------+----------
         3 |        28         43         24         11 |       106 
           |     26.42      40.57      22.64      10.38 |    100.00 
-----------+--------------------------------------------+----------
     Total |       164         80         37         15 |       296 
           |     55.41      27.03      12.50       5.07 |    100.00 

. *TABULATE BY REGION
. tab e_regionpol class_difflevel if period==1 , row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

               Region |               class_difflevel
(politico-geographic) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
E. Europe and C. Asia |        18          5          6          2 |        31 
                      |     58.06      16.13      19.35       6.45 |    100.00 
----------------------+--------------------------------------------+----------
        Latin America |        15          2          3          0 |        20 
                      |     75.00      10.00      15.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                 MENA |        16          3          1          1 |        21 
                      |     76.19      14.29       4.76       4.76 |    100.00 
----------------------+--------------------------------------------+----------
   Sub-Saharan Africa |        42          6          0          2 |        50 
                      |     84.00      12.00       0.00       4.00 |    100.00 
----------------------+--------------------------------------------+----------
W. Europe and N. Amer |        15          9          0          0 |        24 
                      |     62.50      37.50       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
            East Asia |         5          1          0          0 |         6 
                      |     83.33      16.67       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
      South-East Asia |         9          0          1          0 |        10 
                      |     90.00       0.00      10.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
           South Asia |         6          2          0          0 |         8 
                      |     75.00      25.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
          The Pacific |         2          2          0          0 |         4 
                      |     50.00      50.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        The Carribean |         5          0          0          0 |         5 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                Total |       133         30         11          5 |       179 
                      |     74.30      16.76       6.15       2.79 |    100.00 

. tab e_regionpol class_difflevel if period==2 , row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

               Region |               class_difflevel
(politico-geographic) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
E. Europe and C. Asia |        16          6          6          2 |        30 
                      |     53.33      20.00      20.00       6.67 |    100.00 
----------------------+--------------------------------------------+----------
        Latin America |        10          7          3          0 |        20 
                      |     50.00      35.00      15.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                 MENA |        14          4          2          1 |        21 
                      |     66.67      19.05       9.52       4.76 |    100.00 
----------------------+--------------------------------------------+----------
   Sub-Saharan Africa |        31         14          2          3 |        50 
                      |     62.00      28.00       4.00       6.00 |    100.00 
----------------------+--------------------------------------------+----------
W. Europe and N. Amer |        23          0          0          1 |        24 
                      |     95.83       0.00       0.00       4.17 |    100.00 
----------------------+--------------------------------------------+----------
            East Asia |         4          1          0          1 |         6 
                      |     66.67      16.67       0.00      16.67 |    100.00 
----------------------+--------------------------------------------+----------
      South-East Asia |         3          5          1          1 |        10 
                      |     30.00      50.00      10.00      10.00 |    100.00 
----------------------+--------------------------------------------+----------
           South Asia |         5          1          1          1 |         8 
                      |     62.50      12.50      12.50      12.50 |    100.00 
----------------------+--------------------------------------------+----------
          The Pacific |         3          1          0          0 |         4 
                      |     75.00      25.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        The Carribean |         4          1          0          0 |         5 
                      |     80.00      20.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                Total |       113         40         15         10 |       178 
                      |     63.48      22.47       8.43       5.62 |    100.00 

. tab e_regionpol class_difflevel if period==3 , row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

               Region |               class_difflevel
(politico-geographic) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
E. Europe and C. Asia |         7          9          9          5 |        30 
                      |     23.33      30.00      30.00      16.67 |    100.00 
----------------------+--------------------------------------------+----------
        Latin America |         3          5          7          5 |        20 
                      |     15.00      25.00      35.00      25.00 |    100.00 
----------------------+--------------------------------------------+----------
                 MENA |         9          4          4          4 |        21 
                      |     42.86      19.05      19.05      19.05 |    100.00 
----------------------+--------------------------------------------+----------
   Sub-Saharan Africa |        24         10         13          4 |        51 
                      |     47.06      19.61      25.49       7.84 |    100.00 
----------------------+--------------------------------------------+----------
W. Europe and N. Amer |         2         18          2          2 |        24 
                      |      8.33      75.00       8.33       8.33 |    100.00 
----------------------+--------------------------------------------+----------
            East Asia |         1          3          0          2 |         6 
                      |     16.67      50.00       0.00      33.33 |    100.00 
----------------------+--------------------------------------------+----------
      South-East Asia |         5          1          4          0 |        10 
                      |     50.00      10.00      40.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
           South Asia |         3          0          4          1 |         8 
                      |     37.50       0.00      50.00      12.50 |    100.00 
----------------------+--------------------------------------------+----------
          The Pacific |         3          0          1          0 |         4 
                      |     75.00       0.00      25.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        The Carribean |         2          3          0          0 |         5 
                      |     40.00      60.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                Total |        59         53         44         23 |       179 
                      |     32.96      29.61      24.58      12.85 |    100.00 

. *
. tab e_regionpol class_difflevel if period==1 & dem_period_dummy==1, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

               Region |               class_difflevel
(politico-geographic) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
E. Europe and C. Asia |        12          3          1          1 |        17 
                      |     70.59      17.65       5.88       5.88 |    100.00 
----------------------+--------------------------------------------+----------
        Latin America |        12          1          2          0 |        15 
                      |     80.00       6.67      13.33       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                 MENA |         3          1          0          0 |         4 
                      |     75.00      25.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
   Sub-Saharan Africa |         9          2          0          0 |        11 
                      |     81.82      18.18       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
W. Europe and N. Amer |        15          9          0          0 |        24 
                      |     62.50      37.50       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
            East Asia |         4          0          0          0 |         4 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
      South-East Asia |         2          0          1          0 |         3 
                      |     66.67       0.00      33.33       0.00 |    100.00 
----------------------+--------------------------------------------+----------
           South Asia |         2          2          0          0 |         4 
                      |     50.00      50.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
          The Pacific |         2          1          0          0 |         3 
                      |     66.67      33.33       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        The Carribean |         3          0          0          0 |         3 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                Total |        64         19          4          1 |        88 
                      |     72.73      21.59       4.55       1.14 |    100.00 

. tab e_regionpol class_difflevel if period==2 & dem_period_dummy==1, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

               Region |               class_difflevel
(politico-geographic) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
E. Europe and C. Asia |        14          4          3          0 |        21 
                      |     66.67      19.05      14.29       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        Latin America |        10          5          2          0 |        17 
                      |     58.82      29.41      11.76       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                 MENA |         3          1          1          0 |         5 
                      |     60.00      20.00      20.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
   Sub-Saharan Africa |        10          6          1          1 |        18 
                      |     55.56      33.33       5.56       5.56 |    100.00 
----------------------+--------------------------------------------+----------
W. Europe and N. Amer |        23          0          0          1 |        24 
                      |     95.83       0.00       0.00       4.17 |    100.00 
----------------------+--------------------------------------------+----------
            East Asia |         4          0          0          0 |         4 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
      South-East Asia |         1          1          1          1 |         4 
                      |     25.00      25.00      25.00      25.00 |    100.00 
----------------------+--------------------------------------------+----------
           South Asia |         1          1          1          0 |         3 
                      |     33.33      33.33      33.33       0.00 |    100.00 
----------------------+--------------------------------------------+----------
          The Pacific |         2          0          0          0 |         2 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        The Carribean |         4          0          0          0 |         4 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                Total |        72         18          9          3 |       102 
                      |     70.59      17.65       8.82       2.94 |    100.00 

. tab e_regionpol class_difflevel if period==3 & dem_period_dummy==1, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

               Region |               class_difflevel
(politico-geographic) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
E. Europe and C. Asia |         5          9          5          3 |        22 
                      |     22.73      40.91      22.73      13.64 |    100.00 
----------------------+--------------------------------------------+----------
        Latin America |         3          5          5          3 |        16 
                      |     18.75      31.25      31.25      18.75 |    100.00 
----------------------+--------------------------------------------+----------
                 MENA |         2          1          1          1 |         5 
                      |     40.00      20.00      20.00      20.00 |    100.00 
----------------------+--------------------------------------------+----------
   Sub-Saharan Africa |        10          4          6          1 |        21 
                      |     47.62      19.05      28.57       4.76 |    100.00 
----------------------+--------------------------------------------+----------
W. Europe and N. Amer |         2         18          2          2 |        24 
                      |      8.33      75.00       8.33       8.33 |    100.00 
----------------------+--------------------------------------------+----------
            East Asia |         0          3          0          1 |         4 
                      |      0.00      75.00       0.00      25.00 |    100.00 
----------------------+--------------------------------------------+----------
      South-East Asia |         2          0          2          0 |         4 
                      |     50.00       0.00      50.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
           South Asia |         1          0          3          0 |         4 
                      |     25.00       0.00      75.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
          The Pacific |         2          0          0          0 |         2 
                      |    100.00       0.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
        The Carribean |         1          3          0          0 |         4 
                      |     25.00      75.00       0.00       0.00 |    100.00 
----------------------+--------------------------------------------+----------
                Total |        28         43         24         11 |       106 
                      |     26.42      40.57      22.64      10.38 |    100.00 

. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *ASSESS DIFFERENCES IN MEANS FOR QUADRANTS ACROSS PERIODS
. *SECTION C: FOR ALL COUNTRIES
. ************
. **NATIONAL**
. ************
. regress nat_difference i.class_difflevel if period==1, robust cl(country_id)

Linear regression                               Number of obs     =        134
                                                F(3, 133)         =      34.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3577
                                                Root MSE          =      .1049

                              (Std. err. adjusted for 134 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
 nat_difference | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.1365814   .0166159    -8.22   0.000    -.1694471   -.1037157
             3  |  -.2204754   .0318805    -6.92   0.000    -.2835339   -.1574169
             4  |   .0463841   .0371781     1.25   0.214    -.0271529     .119921
                |
          _cons |   .0958277   .0124208     7.72   0.000     .0712598    .1203956
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,   133) =   34.55
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,   133) =   17.58
            Prob > F =    0.0000

. *
. oneway nat_difference class_difflevel if period==1, tabulate

class_diffl |      Summary of nat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |    .0958277   .11671264          90
          2 |  -.04075367   .06056015          30
          3 |  -.12464771   .10059787          11
          4 |   .14221176   .07321726           3
------------+------------------------------------
      Total |    .0481895   .12940918         134

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .796692712      3   .265564237     24.13     0.0000
 Within groups       1.4306231    130   .011004793
------------------------------------------------------------------------
    Total           2.22731581    133   .016746735

Bartlett's equal-variances test: chi2(3) =  14.1514    Prob>chi2 = 0.003

. anova  nat_difference class_difflevel if period==1

                         Number of obs =        134    R-squared     =  0.3577
                         Root MSE      =    .104904    Adj R-squared =  0.3429

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .79669271          3   .26556424     24.13  0.0000
                         |
             class_dif~l |  .79669271          3   .26556424     24.13  0.0000
                         |
                Residual |  1.4306231        130   .01100479  
             ------------+----------------------------------------------------
                   Total |  2.2273158        133   .01674674  

. pwmean nat_difference if period==1, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
 nat_difference |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.1365814   .0221156    -6.18   0.000     -.194139   -.0790238
        3 vs 1  |  -.2204754   .0335069    -6.58   0.000    -.3076795   -.1332713
        4 vs 1  |   .0463841   .0615674     0.75   0.875    -.1138495    .2066176
        3 vs 2  |   -.083894   .0369765    -2.27   0.111    -.1801281      .01234
        4 vs 2  |   .1829654   .0635224     2.88   0.024     .0176438     .348287
        4 vs 3  |   .2668595   .0683279     3.91   0.001     .0890311    .4446878
---------------------------------------------------------------------------------

. 
. *
. regress nat_difference i.class_difflevel if period==2, robust cl(country_id)

Linear regression                               Number of obs     =        149
                                                F(3, 148)         =      62.57
                                                Prob > F          =     0.0000
                                                R-squared         =     0.5093
                                                Root MSE          =     .07675

                              (Std. err. adjusted for 149 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
 nat_difference | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.1421431   .0124591   -11.41   0.000    -.1667637   -.1175225
             3  |  -.1982365   .0246833    -8.03   0.000    -.2470138   -.1494592
             4  |  -.0090762   .0155126    -0.59   0.559     -.039731    .0215786
                |
          _cons |   .0800468   .0093048     8.60   0.000     .0616595    .0984342
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,   148) =   62.57
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,   148) =   48.02
            Prob > F =    0.0000

. 
. oneway nat_difference class_difflevel if period==2, tabulate

class_diffl |      Summary of nat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |   .08004685   .08562215          86
          2 |  -.06209625   .05235236          40
          3 |   -.1181897   .09041477          15
          4 |   .07097063   .03702368           8
------------+------------------------------------
      Total |   .02144361   .10844963         149

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .886594822      3   .295531607     50.17     0.0000
 Within groups      .854080893    145   .005890213
------------------------------------------------------------------------
    Total           1.74067571    148   .011761322

Bartlett's equal-variances test: chi2(3) =  16.4062    Prob>chi2 = 0.001

. anova  nat_difference class_difflevel if period==2

                         Number of obs =        149    R-squared     =  0.5093
                         Root MSE      =    .076748    Adj R-squared =  0.4992

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .88659482          3   .29553161     50.17  0.0000
                         |
             class_dif~l |  .88659482          3   .29553161     50.17  0.0000
                         |
                Residual |  .85408089        145   .00589021  
             ------------+----------------------------------------------------
                   Total |  1.7406757        148   .01176132  

. pwmean nat_difference if period==2, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
 nat_difference |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.1421431   .0146883    -9.68   0.000    -.1803191   -.1039671
        3 vs 1  |  -.1982365   .0214749    -9.23   0.000    -.2540514   -.1424217
        4 vs 1  |  -.0090762   .0283684    -0.32   0.989    -.0828078    .0646554
        3 vs 2  |  -.0560934   .0232365    -2.41   0.079    -.1164869       .0043
        4 vs 2  |   .1330669   .0297243     4.48   0.000     .0558113    .2103224
        4 vs 3  |   .1891603      .0336     5.63   0.000     .1018315    .2764891
---------------------------------------------------------------------------------

. 
. *
. regress nat_difference i.class_difflevel if period==3, robust cl(country_id)

Linear regression                               Number of obs     =        159
                                                F(3, 158)         =      51.70
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4620
                                                Root MSE          =     .11219

                              (Std. err. adjusted for 159 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
 nat_difference | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.1942332   .0242251    -8.02   0.000    -.2420801   -.1463863
             3  |  -.2442933   .0233835   -10.45   0.000    -.2904779   -.1981086
             4  |  -.0399925   .0223788    -1.79   0.076    -.0841927    .0042076
                |
          _cons |   .1118694   .0166449     6.72   0.000     .0789943    .1447445
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,   158) =   51.70
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,   158) =   46.68
            Prob > F =    0.0000

. 
. oneway nat_difference class_difflevel if period==3, tabulate

class_diffl |      Summary of nat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |   .11186938   .11027215          44
          2 |   -.0823638   .12772817          53
          3 |   -.1324239   .10880715          44
          4 |   .07187687   .06447704          18
------------+------------------------------------
      Total |  -.02500564   .15149745         159

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      1.67535179      3   .558450598     44.37     0.0000
 Within groups      1.95098169    155   .012586979
------------------------------------------------------------------------
    Total           3.62633348    158   .022951478

Bartlett's equal-variances test: chi2(3) =   9.3402    Prob>chi2 = 0.025

. anova  nat_difference class_difflevel if period==3

                         Number of obs =        159    R-squared     =  0.4620
                         Root MSE      =    .112192    Adj R-squared =  0.4516

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  1.6753518          3    .5584506     44.37  0.0000
                         |
             class_dif~l |  1.6753518          3    .5584506     44.37  0.0000
                         |
                Residual |  1.9509817        155   .01258698  
             ------------+----------------------------------------------------
                   Total |  3.6263335        158   .02295148  

. pwmean nat_difference if period==3, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
 nat_difference |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.1942332   .0228814    -8.49   0.000    -.2536589   -.1348075
        3 vs 1  |  -.2442933   .0239194   -10.21   0.000    -.3064147   -.1821718
        4 vs 1  |  -.0399925   .0313902    -1.27   0.581    -.1215166    .0415316
        3 vs 2  |  -.0500601   .0228814    -2.19   0.131    -.1094858    .0093656
        4 vs 2  |   .1542407   .0306066     5.04   0.000     .0747515    .2337298
        4 vs 3  |   .2043008   .0313902     6.51   0.000     .1227767    .2858249
---------------------------------------------------------------------------------

. *
. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. regress subnat_difference i.class_difflevel if period==1, robust cl(country_id)

Linear regression                               Number of obs     =        143
                                                F(3, 142)         =      15.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1841
                                                Root MSE          =     .08276

                              (Std. err. adjusted for 143 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
subnat_differ~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |   -.013416   .0149472    -0.90   0.371    -.0429638    .0161317
             3  |  -.1310675   .0219274    -5.98   0.000    -.1744139   -.0877211
             4  |  -.1095151   .0276907    -3.95   0.000    -.1642545   -.0547758
                |
          _cons |   .0438358   .0090524     4.84   0.000     .0259409    .0617307
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,   142) =   15.74
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,   142) =   15.51
            Prob > F =    0.0000

. *
. oneway subnat_difference class_difflevel if period==1, tabulate

class_diffl |    Summary of subnat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |    .0438358   .08925369          99
          2 |   .03041977   .06319037          28
          3 |  -.08723174    .0684929          11
          4 |  -.06567935    .0645016           5
------------+------------------------------------
      Total |   .02729756   .09065166         143

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .214860855      3   .071620285     10.46     0.0000
 Within groups      .952055825    139   .006849322
------------------------------------------------------------------------
    Total           1.16691668    142   .008217723

Bartlett's equal-variances test: chi2(3) =   5.3136    Prob>chi2 = 0.150

. anova subnat_difference class_difflevel if period==1

                         Number of obs =        143    R-squared     =  0.1841
                         Root MSE      =    .082761    Adj R-squared =  0.1665

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .21486086          3   .07162029     10.46  0.0000
                         |
             class_dif~l |  .21486086          3   .07162029     10.46  0.0000
                         |
                Residual |  .95205582        139   .00684932  
             ------------+----------------------------------------------------
                   Total |  1.1669167        142   .00821772  

. pwmean subnat_difference if period==1, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
subnat_differ~e |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |   -.013416   .0177145    -0.76   0.873    -.0594805    .0326485
        3 vs 1  |  -.1310675   .0263031    -4.98   0.000    -.1994655   -.0626695
        4 vs 1  |  -.1095151   .0379348    -2.89   0.023    -.2081602   -.0108701
        3 vs 2  |  -.1176515   .0294497    -3.99   0.001     -.194232   -.0410711
        4 vs 2  |  -.0960991   .0401806    -2.39   0.083    -.2005841    .0083859
        4 vs 3  |   .0215524   .0446378     0.48   0.963    -.0945229    .1376276
---------------------------------------------------------------------------------

. *
. regress subnat_difference i.class_difflevel if period==2, robust cl(country_id)

Linear regression                               Number of obs     =        149
                                                F(3, 148)         =      14.41
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2404
                                                Root MSE          =     .05259

                              (Std. err. adjusted for 149 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
subnat_differ~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.0231245   .0073588    -3.14   0.002    -.0376663   -.0085827
             3  |  -.0852283   .0179486    -4.75   0.000    -.1206969   -.0497596
             4  |  -.0726214   .0137788    -5.27   0.000    -.0998501   -.0453928
                |
          _cons |   .0329589   .0063949     5.15   0.000     .0203218     .045596
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,   148) =   14.41
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,   148) =   13.30
            Prob > F =    0.0000

. *
. oneway subnat_difference class_difflevel if period==2, tabulate

class_diffl |    Summary of subnat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |    .0329589   .05951804          88
          2 |   .00983438   .02185603          36
          3 |  -.05226937   .06632397          15
          4 |  -.03966255   .04013349          10
------------+------------------------------------
      Total |   .01391782   .05972423         149

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .126925517      3   .042308506     15.30     0.0000
 Within groups      .400987982    145   .002765434
------------------------------------------------------------------------
    Total             .5279135    148   .003566983

Bartlett's equal-variances test: chi2(3) =  37.3888    Prob>chi2 = 0.000

. anova subnat_difference class_difflevel if period==2

                         Number of obs =        149    R-squared     =  0.2404
                         Root MSE      =    .052587    Adj R-squared =  0.2247

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .12692552          3   .04230851     15.30  0.0000
                         |
             class_dif~l |  .12692552          3   .04230851     15.30  0.0000
                         |
                Residual |  .40098798        145   .00276543  
             ------------+----------------------------------------------------
                   Total |   .5279135        148   .00356698  

. pwmean subnat_difference if period==2, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
subnat_differ~e |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.0231245    .010404    -2.22   0.122    -.0501653    .0039162
        3 vs 1  |  -.0852283   .0146897    -5.80   0.000    -.1234079   -.0470486
        4 vs 1  |  -.0726214    .017549    -4.14   0.000    -.1182327   -.0270102
        3 vs 2  |  -.0621038   .0161611    -3.84   0.001    -.1041075      -.0201
        4 vs 2  |  -.0494969   .0187979    -2.63   0.046    -.0983541   -.0006398
        4 vs 3  |   .0126068   .0214687     0.59   0.936     -.043192    .0684056
---------------------------------------------------------------------------------

. *
. regress subnat_difference i.class_difflevel if period==3, robust cl(country_id)

Linear regression                               Number of obs     =        149
                                                F(3, 148)         =      37.30
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4727
                                                Root MSE          =     .06647

                              (Std. err. adjusted for 149 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
subnat_differ~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.0517596   .0111801    -4.63   0.000    -.0738529   -.0296663
             3  |  -.1453624   .0153526    -9.47   0.000    -.1757009   -.1150238
             4  |   -.141097   .0207718    -6.79   0.000    -.1821446   -.1000493
                |
          _cons |    .067041   .0099178     6.76   0.000     .0474422    .0866399
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,   148) =   37.30
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,   148) =   34.45
            Prob > F =    0.0000

. *
. oneway subnat_difference class_difflevel if period==3, tabulate

class_diffl |    Summary of subnat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |   .06704104   .06417481          42
          2 |   .01528147   .03260764          40
          3 |  -.07832134   .07757197          44
          4 |  -.07405592   .08828715          23
------------+------------------------------------
      Total |  -.01156002   .09059843         149

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .574243126      3   .191414375     43.33     0.0000
 Within groups      .640552053    145     .0044176
------------------------------------------------------------------------
    Total           1.21479518    148   .008208076

Bartlett's equal-variances test: chi2(3) =  32.2388    Prob>chi2 = 0.000

. anova subnat_difference class_difflevel if period==3

                         Number of obs =        149    R-squared     =  0.4727
                         Root MSE      =    .066465    Adj R-squared =  0.4618

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .57424313          3   .19141438     43.33  0.0000
                         |
             class_dif~l |  .57424313          3   .19141438     43.33  0.0000
                         |
                Residual |  .64055205        145    .0044176  
             ------------+----------------------------------------------------
                   Total |  1.2147952        148   .00820808  

. pwmean subnat_difference if period==3, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
subnat_differ~e |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.0517596    .014684    -3.52   0.003    -.0899245   -.0135947
        3 vs 1  |  -.1453624   .0143381   -10.14   0.000    -.1826281   -.1080966
        4 vs 1  |   -.141097    .017241    -8.18   0.000    -.1859075   -.0962864
        3 vs 2  |  -.0936028   .0145203    -6.45   0.000    -.1313422   -.0558634
        4 vs 2  |  -.0893374   .0173928    -5.14   0.000    -.1345426   -.0441322
        4 vs 3  |   .0042654   .0171017     0.25   0.995    -.0401833    .0487141
---------------------------------------------------------------------------------

. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *SECTION D: FOR DEMOCRACIES ONLY
. ************
. **NATIONAL**
. ************
. regress nat_difference i.class_difflevel if period==1 & dem_period_dummy==1, robust cl(country_id)

Linear regression                               Number of obs     =         78
                                                F(2, 77)          =      17.02
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2238
                                                Root MSE          =     .09704

                               (Std. err. adjusted for 78 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
 nat_difference | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.0987177   .0179914    -5.49   0.000    -.1345432   -.0628923
             3  |  -.1572919   .0470615    -3.34   0.001    -.2510033   -.0635804
                |
          _cons |   .0797273   .0148289     5.38   0.000     .0501993    .1092554
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0

       F(  2,    77) =   17.02
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0

       F(  1,    77) =    1.63
            Prob > F =    0.2049

. *
. oneway nat_difference class_difflevel if period==1 & dem_period_dummy==1, tabulate

class_diffl |      Summary of nat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |   .07972732   .10883211          55
          2 |  -.01899043    .0447394          19
          3 |  -.07756454   .10114446           4
------------+------------------------------------
      Total |   .04761444    .1087117          78

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .203685105      2   .101842553     10.81     0.0001
 Within groups      .706318825     75   .009417584
------------------------------------------------------------------------
    Total            .91000393     77   .011818233

Bartlett's equal-variances test: chi2(2) =  14.3063    Prob>chi2 = 0.001

. anova  nat_difference class_difflevel if period==1 & dem_period_dummy==1

                         Number of obs =         78    R-squared     =  0.2238
                         Root MSE      =    .097044    Adj R-squared =  0.2031

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .20368511          2   .10184255     10.81  0.0001
                         |
             class_dif~l |  .20368511          2   .10184255     10.81  0.0001
                         |
                Residual |  .70631883         75   .00941758  
             ------------+----------------------------------------------------
                   Total |  .91000393         77   .01181823  

. pwmean nat_difference if period==1 & dem_period_dummy==1, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            3
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
 nat_difference |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.0987177   .0258242    -3.82   0.001    -.1604664   -.0369691
        3 vs 1  |  -.1572919   .0502556    -3.13   0.007    -.2774587    -.037125
        3 vs 2  |  -.0585741   .0533859    -1.10   0.519     -.186226    .0690777
---------------------------------------------------------------------------------

. 
. *
. regress nat_difference i.class_difflevel if period==2 & dem_period_dummy==1, robust cl(country_id)

Linear regression                               Number of obs     =         95
                                                F(3, 94)          =      40.63
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4977
                                                Root MSE          =       .069

                               (Std. err. adjusted for 95 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
 nat_difference | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.1342168   .0156494    -8.58   0.000    -.1652891   -.1031445
             3  |  -.1752771   .0226381    -7.74   0.000    -.2202256   -.1303286
             4  |  -.0180515   .0131394    -1.37   0.173    -.0441402    .0080372
                |
          _cons |   .0698288   .0093035     7.51   0.000     .0513564    .0883012
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,    94) =   40.63
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,    94) =   41.65
            Prob > F =    0.0000

. *
. oneway nat_difference class_difflevel if period==2 & dem_period_dummy==1, tabulate

class_diffl |      Summary of nat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |   .06982882   .07398282          65
          2 |    -.064388   .05376681          18
          3 |  -.10544826   .06427245           9
          4 |   .05177732   .01926366           3
------------+------------------------------------
      Total |   .02722302   .09578997          95

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .429281713      3   .143093904     30.06     0.0000
 Within groups      .433235839     91   .004760833
------------------------------------------------------------------------
    Total           .862517552     94   .009175719

Bartlett's equal-variances test: chi2(3) =   5.3807    Prob>chi2 = 0.146

. anova  nat_difference class_difflevel if period==2 & dem_period_dummy==1

                         Number of obs =         95    R-squared     =  0.4977
                         Root MSE      =    .068999    Adj R-squared =  0.4811

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .42928171          3    .1430939     30.06  0.0000
                         |
             class_dif~l |  .42928171          3    .1430939     30.06  0.0000
                         |
                Residual |  .43323584         91   .00476083  
             ------------+----------------------------------------------------
                   Total |  .86251755         94   .00917572  

. pwmean nat_difference if period==2 & dem_period_dummy==1, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
 nat_difference |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.1342168   .0183775    -7.30   0.000    -.1823135   -.0861201
        3 vs 1  |  -.1752771   .0245403    -7.14   0.000    -.2395025   -.1110516
        4 vs 1  |  -.0180515   .0407454    -0.44   0.971    -.1246881    .0885851
        3 vs 2  |  -.0410603   .0281686    -1.46   0.467    -.1147817    .0326612
        4 vs 2  |   .1161653   .0430283     2.70   0.041      .003554    .2287766
        4 vs 3  |   .1572256   .0459992     3.42   0.005      .036839    .2776121
---------------------------------------------------------------------------------

. *
. regress nat_difference i.class_difflevel if period==3 & dem_period_dummy==1, robust cl(country_id)

Linear regression                               Number of obs     =        103
                                                F(3, 102)         =      38.86
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4426
                                                Root MSE          =     .11384

                              (Std. err. adjusted for 103 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
 nat_difference | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |   -.200066    .028486    -7.02   0.000    -.2565679   -.1435641
             3  |  -.2493167   .0279388    -8.92   0.000    -.3047332   -.1939002
             4  |  -.0551078   .0225527    -2.44   0.016     -.099841   -.0103745
                |
          _cons |   .1173948   .0189802     6.19   0.000     .0797477    .1550418
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,   102) =   38.86
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,   102) =   41.09
            Prob > F =    0.0000

. *
. oneway nat_difference class_difflevel if period==3 & dem_period_dummy==1, tabulate

class_diffl |      Summary of nat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |   .11739478   .09676141          26
          2 |  -.08267119   .13817543          43
          3 |  -.13192191   .10058697          24
          4 |   .06228698   .03980748          10
------------+------------------------------------
      Total |  -.02957139   .15021339         103

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      1.01861243      3   .339537477     26.20     0.0000
 Within groups      1.28292186     99   .012958807
------------------------------------------------------------------------
    Total           2.30153429    102   .022564062

Bartlett's equal-variances test: chi2(3) =  16.0829    Prob>chi2 = 0.001

. anova  nat_difference class_difflevel if period==3 & dem_period_dummy==1

                         Number of obs =        103    R-squared     =  0.4426
                         Root MSE      =    .113837    Adj R-squared =  0.4257

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  1.0186124          3   .33953748     26.20  0.0000
                         |
             class_dif~l |  1.0186124          3   .33953748     26.20  0.0000
                         |
                Residual |  1.2829219         99   .01295881  
             ------------+----------------------------------------------------
                   Total |  2.3015343        102   .02256406  

. pwmean nat_difference if period==3 & dem_period_dummy==1, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
 nat_difference |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |   -.200066   .0282804    -7.07   0.000    -.2739686   -.1261633
        3 vs 1  |  -.2493167   .0322237    -7.74   0.000    -.3335239   -.1651095
        4 vs 1  |  -.0551078   .0423591    -1.30   0.565     -.165801    .0555855
        3 vs 2  |  -.0492507   .0290055    -1.70   0.330    -.1250481    .0265466
        4 vs 2  |   .1449582   .0399656     3.63   0.003     .0405198    .2493966
        4 vs 3  |   .1942089   .0428466     4.53   0.000     .0822418     .306176
---------------------------------------------------------------------------------

. *
. ************
. *SUBNATIONAL
. ************
. regress subnat_difference i.class_difflevel if period==1 & dem_period_dummy==1, robust cl(country_id)

Linear regression                               Number of obs     =         78
                                                F(2, 77)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0906
                                                Root MSE          =     .08978

                               (Std. err. adjusted for 78 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
subnat_differ~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.0337867    .016407    -2.06   0.043    -.0664573   -.0011162
             3  |  -.1059758   .0318919    -3.32   0.001    -.1694806    -.042471
             4  |  -.1031207   .0137429    -7.50   0.000    -.1304864    -.075755
                |
          _cons |   .0486703   .0137429     3.54   0.001     .0213046     .076036
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,    77) =   38.72
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,    77) =   29.93
            Prob > F =    0.0000

. *
. oneway subnat_difference class_difflevel if period==1 & dem_period_dummy==1, tabulate

class_diffl |    Summary of subnat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |   .04867032   .10107759          56
          2 |   .01488358   .03709996          17
          3 |   -.0573055   .06473531           4
          4 |  -.05445039           0           1
------------+------------------------------------
      Total |   .03454983   .09229563          78

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .059411331      3   .019803777      2.46     0.0696
 Within groups      .596511837     74   .008060971
------------------------------------------------------------------------
    Total           .655923167     77   .008518483

Bartlett's equal-variances test: chi2(2) =  16.1323    Prob>chi2 = 0.000

note: Bartlett's test performed on cells with positive variance:
      1 single-observation cells not used

. anova subnat_difference class_difflevel if period==1 & dem_period_dummy==1

                         Number of obs =         78    R-squared     =  0.0906
                         Root MSE      =    .089783    Adj R-squared =  0.0537

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .05941133          3   .01980378      2.46  0.0696
                         |
             class_dif~l |  .05941133          3   .01980378      2.46  0.0696
                         |
                Residual |  .59651184         74   .00806097  
             ------------+----------------------------------------------------
                   Total |  .65592317         77   .00851848  

. pwmean subnat_difference if period==1 & dem_period_dummy==1, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
subnat_differ~e |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.0337867    .024862    -1.36   0.529    -.0991337    .0315602
        3 vs 1  |  -.1059758   .0464671    -2.28   0.112    -.2281091    .0161575
        4 vs 1  |  -.1031207    .090581    -1.14   0.667    -.3412024    .1349609
        3 vs 2  |  -.0721891   .0498941    -1.45   0.475    -.2033298    .0589517
        4 vs 2  |   -.069334   .0923859    -0.75   0.876    -.3121595    .1734915
        4 vs 3  |   .0028551   .1003803     0.03   1.000     -.260983    .2666932
---------------------------------------------------------------------------------

. *
. regress subnat_difference i.class_difflevel if period==2 & dem_period_dummy==1, robust cl(country_id)

Linear regression                               Number of obs     =         93
                                                F(3, 92)          =       6.26
                                                Prob > F          =     0.0006
                                                R-squared         =     0.1681
                                                Root MSE          =     .04306

                               (Std. err. adjusted for 93 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
subnat_differ~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.0102972   .0075326    -1.37   0.175    -.0252577    .0046632
             3  |  -.0597577   .0196468    -3.04   0.003    -.0987779   -.0207376
             4  |  -.0499417   .0146102    -3.42   0.001    -.0789588   -.0209247
                |
          _cons |   .0230482    .005831     3.95   0.000     .0114674     .034629
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,    92) =    6.26
            Prob > F =    0.0006

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,    92) =    6.61
            Prob > F =    0.0021

. *
. oneway subnat_difference class_difflevel if period==2 & dem_period_dummy==1, tabulate

class_diffl |    Summary of subnat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |   .02304815   .04563927          63
          2 |   .01275092   .02036576          18
          3 |  -.03670955   .05840086           9
          4 |   -.0268936   .02779966           3
------------+------------------------------------
      Total |   .01366111   .04643363          93

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .033335142      3   .011111714      5.99     0.0009
 Within groups      .165024364     89   .001854206
------------------------------------------------------------------------
    Total           .198359506     92   .002156082

Bartlett's equal-variances test: chi2(3) =  14.0453    Prob>chi2 = 0.003

. anova subnat_difference class_difflevel if period==2 & dem_period_dummy==1

                         Number of obs =         93    R-squared     =  0.1681
                         Root MSE      =     .04306    Adj R-squared =  0.1400

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .03333514          3   .01111171      5.99  0.0009
                         |
             class_dif~l |  .03333514          3   .01111171      5.99  0.0009
                         |
                Residual |  .16502436         89   .00185421  
             ------------+----------------------------------------------------
                   Total |  .19835951         92   .00215608  

. pwmean subnat_difference if period==2 & dem_period_dummy==1, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
subnat_differ~e |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.0102972   .0115084    -0.89   0.808     -.040429    .0198345
        3 vs 1  |  -.0597577   .0153445    -3.89   0.001    -.0999334    -.019582
        4 vs 1  |  -.0499417    .025446    -1.96   0.210    -.1165656    .0166821
        3 vs 2  |  -.0494605   .0175794    -2.81   0.030    -.0954875   -.0034334
        4 vs 2  |  -.0396445   .0268529    -1.48   0.456     -.109952     .030663
        4 vs 3  |    .009816    .028707     0.34   0.986    -.0653459    .0849778
---------------------------------------------------------------------------------

. *
. regress subnat_difference i.class_difflevel if period==3 & dem_period_dummy==1, robust cl(country_id)

Linear regression                               Number of obs     =         92
                                                F(3, 91)          =      19.16
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4729
                                                Root MSE          =     .06301

                               (Std. err. adjusted for 92 clusters in country_id)
---------------------------------------------------------------------------------
                |               Robust
subnat_differ~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
             2  |  -.0635292     .01754    -3.62   0.000    -.0983702   -.0286882
             3  |  -.1547444   .0236145    -6.55   0.000    -.2016517    -.107837
             4  |  -.1367193    .024976    -5.47   0.000     -.186331   -.0871076
                |
          _cons |   .0772318   .0166487     4.64   0.000     .0441611    .1103025
---------------------------------------------------------------------------------

. testparm i.class_difflevel

 ( 1)  2.class_difflevel = 0
 ( 2)  3.class_difflevel = 0
 ( 3)  4.class_difflevel = 0

       F(  3,    91) =   19.16
            Prob > F =    0.0000

. testparm i.class_difflevel, equal

 ( 1)  - 2.class_difflevel + 3.class_difflevel = 0
 ( 2)  - 2.class_difflevel + 4.class_difflevel = 0

       F(  2,    91) =   18.90
            Prob > F =    0.0000

. *
. oneway subnat_difference class_difflevel if period==3 & dem_period_dummy==1, tabulate

class_diffl |    Summary of subnat_difference
       evel |        Mean   Std. dev.       Freq.
------------+------------------------------------
          1 |   .07723183   .07645983          21
          2 |   .01370265    .0328509          36
          3 |  -.07751252   .08196596          24
          4 |  -.05948745   .06333827          11
------------+------------------------------------
      Total |  -.00434238   .08533978          92

                        Analysis of variance
    Source              SS         df      MS            F     Prob > F
------------------------------------------------------------------------
Between groups      .313407445      3   .104469148     26.32     0.0000
 Within groups      .349334457     88    .00396971
------------------------------------------------------------------------
    Total           .662741902     91   .007282878

Bartlett's equal-variances test: chi2(3) =  25.0521    Prob>chi2 = 0.000

. anova subnat_difference class_difflevel if period==3 & dem_period_dummy==1

                         Number of obs =         92    R-squared     =  0.4729
                         Root MSE      =    .063006    Adj R-squared =  0.4549

                  Source | Partial SS         df         MS        F    Prob>F
             ------------+----------------------------------------------------
                   Model |  .31340744          3   .10446915     26.32  0.0000
                         |
             class_dif~l |  .31340744          3   .10446915     26.32  0.0000
                         |
                Residual |  .34933446         88   .00396971  
             ------------+----------------------------------------------------
                   Total |   .6627419         91   .00728288  

. pwmean subnat_difference if period==3 & dem_period_dummy==1, over(class_difflevel) mcompare(tukey) effects

Pairwise comparisons of means with equal variances

Over: class_difflevel

------------------------------
                |    Number of
                |  comparisons
----------------+-------------
class_difflevel |            6
------------------------------

---------------------------------------------------------------------------------
                |                              Tukey                Tukey
subnat_differ~e |   Contrast   Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
class_difflevel |
        2 vs 1  |  -.0635292   .0173004    -3.67   0.002    -.1088356   -.0182228
        3 vs 1  |  -.1547444   .0188265    -8.22   0.000    -.2040474   -.1054413
        4 vs 1  |  -.1367193   .0234503    -5.83   0.000    -.1981311   -.0753074
        3 vs 2  |  -.0912152   .0166034    -5.49   0.000    -.1346964   -.0477339
        4 vs 2  |  -.0731901    .021706    -3.37   0.006    -.1300341   -.0163461
        4 vs 3  |   .0180251    .022941     0.79   0.861    -.0420529     .078103
---------------------------------------------------------------------------------

. **************************************************************************************************************************
> ************************
. **************************************************************************************************************************
> ************************
. *SECTION E: EXCLUSION ZONES
. *
. *DELIMIT THE UPPER AND LOWER BOUNDS OF THE Y AXIS (THE HEIGHT OF THE SQUARE)
. *Y_N is for the N number of exclusion zones.
. *WE DO THIS PER PERIOD 
. *
. *PERIOD 1
. sum  subnat_difference  if period==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
subnat_dif~e |        143    .0272976    .0906517  -.1782346   .4817272

. gen  p1_y1_high=. 
(536 missing values generated)

. gen  p1_y1_low=.
(536 missing values generated)

. gen  p1_y2_high=. 
(536 missing values generated)

. gen  p1_y2_low=.
(536 missing values generated)

. *
. ***VALUES ARE OBTAINED WITH ((Y_STDDEV+X_STDDEV)/2)/2----->HALF OF THE AVERAGED STD DEVIATIONS. 
. *HALF OF THE AVERAGED STD DEVIATIONS==0.055
. replace  p1_y1_high= 0.055      
(536 real changes made)

. replace  p1_y1_low= -0.055      
(536 real changes made)

. replace  p1_y2_high= 0.22               
(536 real changes made)

. replace  p1_y2_low= -0.22
(536 real changes made)

. *
. *NATIONAL*
. *PERIOD 1
. sum  nat_difference     if period==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
nat_differ~e |        134    .0481895    .1294092  -.2786885   .4557701

. gen p1_x1_high=.
(536 missing values generated)

. gen p1_x1_low=.
(536 missing values generated)

. gen p1_x2_high=.
(536 missing values generated)

. gen p1_x2_low=.
(536 missing values generated)

. *
. replace p1_x1_high=  0.055      
(536 real changes made)

. replace p1_x1_low = -0.055      
(536 real changes made)

. replace p1_x2_high= 0.22                
(536 real changes made)

. replace p1_x2_low= -0.22
(536 real changes made)

. *PERIOD 2
. *Subnatioanl PERIOD 2
. sum  subnat_difference  if period==2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
subnat_dif~e |        149    .0139178    .0597242  -.1881078   .2471265

. gen  p2_y1_high=. 
(536 missing values generated)

. gen  p2_y1_low=.
(536 missing values generated)

. gen  p2_y2_high=. 
(536 missing values generated)

. gen  p2_y2_low=.
(536 missing values generated)

. *
. replace  p2_y1_high= 0.04204345
(536 real changes made)

. replace  p2_y1_low= -0.04204345         
(536 real changes made)

. replace  p2_y2_high=  0.164810329               
(536 real changes made)

. replace  p2_y2_low=  -0.16481032
(536 real changes made)

. *National PERIOD 2
. sum  nat_difference     if period==2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
nat_differ~e |        149    .0214436    .1084496  -.2937024   .4595034

. gen  p2_x1_high=. 
(536 missing values generated)

. gen  p2_x1_low=.
(536 missing values generated)

. gen  p2_x2_high=. 
(536 missing values generated)

. gen  p2_x2_low=.
(536 missing values generated)

. *
. replace p2_x1_high=  0.04204345         
(536 real changes made)

. replace p2_x1_low = -0.04204345
(536 real changes made)

. replace p2_x2_high=  0.16481032 
(536 real changes made)

. replace p2_x2_low=  -0.16481032
(536 real changes made)

. *PERIOD 3
. *SUBNATIONAL PERIOD 3
. sum  subnat_difference  if period==3

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
subnat_dif~e |        149     -.01156    .0905984  -.3875626   .3094607

. gen  p3_y1_high=. 
(536 missing values generated)

. gen  p3_y1_low=.
(536 missing values generated)

. gen  p3_y2_high=. 
(536 missing values generated)

. gen  p3_y2_low=.
(536 missing values generated)

. *
. replace  p3_y1_high= 0.06052398
(536 real changes made)

. replace  p3_y1_low= -0.06052398
(536 real changes made)

. replace  p3_y2_high= 0.23725398
(536 real changes made)

. replace  p3_y2_low= -0.23725398
(536 real changes made)

. *
. *NATIONAL PERIOD 3
. sum  nat_difference     if period==3

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
nat_differ~e |        159   -.0250056    .1514975  -.7360818   .5466645

. gen  p3_x1_high=. 
(536 missing values generated)

. gen  p3_x1_low=.
(536 missing values generated)

. gen  p3_x2_high=. 
(536 missing values generated)

. gen  p3_x2_low=.
(536 missing values generated)

. *
. replace  p3_x1_high= 0.06052398
(536 real changes made)

. replace  p3_x1_low= -0.06052398
(536 real changes made)

. replace  p3_x2_high= 0.23725398
(536 real changes made)

. replace  p3_x2_low= -0.23725398
(536 real changes made)

. *
. *
. *GRAPHS
. *PERIOD 1
. gen exclude_p1=.
(536 missing values generated)

. replace exclude_p1=1 if period==1 & subnat_difference>=p1_y1_high
(63 real changes made)

. replace exclude_p1=1 if period==1 & subnat_difference<=p1_y1_low
(9 real changes made)

. replace exclude_p1=1 if period==1 & nat_difference<=p1_x1_low
(7 real changes made)

. replace exclude_p1=1 if period==1 & nat_difference>=p1_x1_high
(40 real changes made)

. replace exclude_p1=0 if exclude_p1==.
(417 real changes made)

. *
. gen include_p1=.
(536 missing values generated)

. replace include_p1=1 if period==1 & subnat_difference<=p1_y2_high & nat_difference<=p1_x2_high 
(108 real changes made)

. replace include_p1=0 if period==1 & nat_difference<=p1_x2_low
(2 real changes made)

. replace include_p1=0 if include_p1==.
(428 real changes made)

. *
. scatter subnat_difference nat_difference if period==1, yline(0, lpat(dash) lcol(gs7)) /// 
> xline(0, lpat(dash) lcol(gs7)) jitter(1) graphregion(margin(2 2 2 2)) plotregion(margin(0.3 0.3 0.3 0.3)) || /// 
> rarea p1_y1_low p1_y1_high nat_difference if period==1 & nat_difference>p1_x1_low & nat_difference<p1_x1_high, sort color(
> navy%60) || ///
> rarea p1_y2_low p1_y2_high nat_difference if period==1 & nat_difference>p1_x2_low & nat_difference<p1_x2_high, sort color(
> purple%20) ///
> yscale(titlegap(0)) ysize(1) xsize(1) leg(pos(5) ring(0) row(2) size(small) order(2 "1/2 Avg.Std.Dev." 3 "2 Avg.Std.Dev"))
>  /// 
> subtitle("1990-2000", pos(12)) xtitle("Nat. Free & Fair {&Delta} ") ytitle("Subnat. Free & Fair {&Delta}") ///
> msymbol(circle_hollow) mlw(vthin) mlcol(black)  msize(small) scheme(lean1) /// 
> yscale(titlegap(0)) ylabel(-.6(.2).8) xlabel(-.6(.2).8)

. *
. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. tab class_difflevel if exclude_p1==1 /*OUTSIDE BLUE ZONE*/

class_diffl |
       evel |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         92       77.31       77.31
          2 |         13       10.92       88.24
          3 |          9        7.56       95.80
          4 |          5        4.20      100.00
------------+-----------------------------------
      Total |        119      100.00

. tab class_difflevel if include_p1==1 /*INSIDE PURPLE ZONE*/

class_diffl |
       evel |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         67       63.21       63.21
          2 |         27       25.47       88.68
          3 |          9        8.49       97.17
          4 |          3        2.83      100.00
------------+-----------------------------------
      Total |        106      100.00

. *
. *
. *PERIOD 2
. gen exclude_p2=.
(536 missing values generated)

. replace exclude_p2=1 if period==2 & subnat_difference>=p2_y1_high
(54 real changes made)

. replace exclude_p2=1 if period==2 & subnat_difference<=p2_y1_low
(9 real changes made)

. replace exclude_p2=1 if period==2 & nat_difference<=p2_x1_low
(27 real changes made)

. replace exclude_p2=1 if period==2 & nat_difference>=p2_x1_high
(51 real changes made)

. replace exclude_p2=0 if exclude_p2==.
(395 real changes made)

. *
. gen include_p2=.
(536 missing values generated)

. replace include_p2=1 if period==2 & subnat_difference<p2_y2_high & nat_difference<p2_x2_high 
(124 real changes made)

. replace include_p2=0 if period==2 & nat_difference<p2_x2_low
(6 real changes made)

. replace include_p2=0 if period==2 & subnat_difference<p2_y2_low
(1 real change made)

. replace include_p2=0 if include_p2==.
(412 real changes made)

. *
. scatter subnat_difference nat_difference if period==2, yline(0, lpat(dash) lcol(gs7)) /// 
> xline(0, lpat(dash) lcol(gs7)) jitter(1) graphregion(margin(2 2 2 2)) plotregion(margin(0.3 0.3 0.3 0.3)) || /// 
> rarea p2_y1_low p2_y1_high nat_difference if period==2 & nat_difference>p2_x1_low & nat_difference<p2_x1_high, sort color(
> navy%60) || ///
> rarea p2_y2_low p2_y2_high nat_difference if period==2 & nat_difference>p2_x2_low & nat_difference<p2_x2_high, sort color(
> purple%20) ///
> yscale(titlegap(0)) ysize(1) xsize(1) leg(pos(5) ring(0) row(2) size(small) order(2 "1/2 Avg.Std.Dev." 3 "2 Avg.Std.Dev"))
>  /// 
> subtitle("2000-2010", pos(12)) xtitle("Nat. Free & Fair {&Delta} ") ytitle("Subnat. Free & Fair {&Delta}") ///
> msymbol(circle_hollow) mlw(vthin) mlcol(black)  msize(small) scheme(lean1) /// 
> yscale(titlegap(0)) ylabel(-.6(.2).8) xlabel(-.6(.2).8)

. *
. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. tab class_difflevel if exclude_p2==1 /*OUTSIDE BLUE ZONE*/

class_diffl |
       evel |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         92       65.25       65.25
          2 |         27       19.15       84.40
          3 |         12        8.51       92.91
          4 |         10        7.09      100.00
------------+-----------------------------------
      Total |        141      100.00

. tab class_difflevel if include_p2==1 /*INSIDE PURPLE ZONE*/

class_diffl |
       evel |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         65       55.56       55.56
          2 |         34       29.06       84.62
          3 |         10        8.55       93.16
          4 |          8        6.84      100.00
------------+-----------------------------------
      Total |        117      100.00

. *
. 
end of do-file

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. gen exclude_p3=.
(536 missing values generated)

. replace exclude_p3=1 if period==3 & subnat_difference>=p3_y1_high
(50 real changes made)

. replace exclude_p3=1 if period==3 & subnat_difference<=p3_y1_low
(29 real changes made)

. replace exclude_p3=1 if period==3 & nat_difference<=p3_x1_low
(24 real changes made)

. replace exclude_p3=1 if period==3 & nat_difference>=p3_x1_high
(20 real changes made)

. replace exclude_p3=0 if exclude_p3==.
(413 real changes made)

. *
. gen include_p3=.
(536 missing values generated)

. replace include_p3=1 if period==3 & subnat_difference<p3_y2_high & nat_difference<p3_x2_high 
(132 real changes made)

. replace include_p3=0 if period==3 & nat_difference<p3_x2_low
(12 real changes made)

. replace include_p3=0 if period==3 & subnat_difference<p3_y2_low
(1 real change made)

. replace include_p3=0 if include_p3==.
(402 real changes made)

. *
. scatter subnat_difference nat_difference if period==3, yline(0, lpat(dash) lcol(gs7)) /// 
> xline(0, lpat(dash) lcol(gs7)) jitter(1) graphregion(margin(2 2 2 2)) plotregion(margin(0.3 0.3 0.3 0.3)) || /// 
> rarea p3_y1_low p3_y1_high nat_difference if period==3 & nat_difference>p3_x1_low & nat_difference<p3_x1_high, sort color(
> navy%60) || ///
> rarea p3_y2_low p3_y2_high nat_difference if period==3 & nat_difference>p3_x2_low & nat_difference<p3_x2_high, sort color(
> purple%20) ///
> yscale(titlegap(0)) ysize(1) xsize(1) leg(pos(5) ring(0) row(2) size(small) order(2 "1/2 Avg.Std.Dev." 3 "2 Avg.Std.Dev"))
>  /// 
> subtitle("2010-2022", pos(12)) xtitle("Nat. Free & Fair {&Delta} ") ytitle("Subnat. Free & Fair {&Delta}") ///
> msymbol(circle_hollow) mlw(vthin) mlcol(black)  msize(small) scheme(lean1) /// 
> yscale(titlegap(0)) ylabel(-.6(.2).8) xlabel(-.6(.2).8) 

. *
. *
. tab class_difflevel if exclude_p3==1 /*OUTSIDE BLUE ZONE*/

class_diffl |
       evel |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         46       37.40       37.40
          2 |         24       19.51       56.91
          3 |         36       29.27       86.18
          4 |         17       13.82      100.00
------------+-----------------------------------
      Total |        123      100.00

. tab class_difflevel if include_p3==1 /*INSIDE PURPLE ZONE*/

class_diffl |
       evel |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         31       25.62       25.62
          2 |         38       31.40       57.02
          3 |         36       29.75       86.78
          4 |         16       13.22      100.00
------------+-----------------------------------
      Total |        121      100.00

. **************************************************************************************************************************
> ************************
. 
end of do-file

. use "/Users/javier/Desktop/Papers/1. Under Review/Subnational Decoupling/1. Perspectives Submission/Review Round_2/TEXT, T
> ABLES AND FIGURES/0Replication Files/DifferenceAltMeasures.dta", clear

. do "/var/folders/tf/1s8d96c57f1cq5xqmrqqd0fm0000gn/T//SD57971.000000"

. *     
. *GRPAHS FOR ISED
. scatter subnatdiff_ised natdiff_vanhanen,  xline(0, lpat(shortdash) lcol(gs10)) yline(0,lpat(shortdash) lcol(gs10)) ///
> scheme(lean1) colorvar(period) colordiscrete coloruseplegend /// 
> plegend(order(1 "1990-2000" 2 "2000-2010" 3 "2010-2022") col(3) pos(5) ring(0) size(small)) ///
> xtitle("Nat. Dem. {&Delta} Using Vanhanen", size(small)) xlab(,labsize(small)) /// 
> ytitle("Subnat. Dem. {&Delta} Using ISED", size(small)) ylab(,labsize(small)) ///
> jitter(1) graphregion(margin(2 2 2 2)) plotregion(margin(0.3 0.3 0.3 0.3)) /// 
> yscale(titlegap(0)) ysize(1) xsize(1) ylabel(-.4(.2).4) xlabel(-.4(.2).4)

. *
. *GRAPH FOR FIDALGO
. scatter subnatdiff_fidalgo natddiff_vdempol, xline(0, lpat(shortdash) lcol(gs10)) yline(0,lpat(shortdash) lcol(gs10)) ///
> scheme(lean1) colorvar(period) colordiscrete coloruseplegend /// 
> plegend(order(1 "1990-2000" 2 "2000-2010" 3 "2010-2016") col(3) pos(5) ring(0) size(small)) ///
> xtitle("Nat. Dem. {&Delta} Using V-Dem Polyarchy", size(small)) xlab(,labsize(small)) /// 
> ytitle("Subnat. Dem. {&Delta} Using SEDS", size(small)) ylab(,labsize(small)) ///
> jitter(2) graphregion(margin(2 2 2 2)) plotregion(margin(0.3 0.3 0.3 0.3)) /// 
> yscale(titlegap(0)) ysize(1) xsize(1) ylabel(-.4(.2).4) xlabel(-.4(.2).4)

. *
. *
. *GENERATE QUADRIANT CLASSIFICATION ISED
. gen class_ISED_difflevel=. 
(54 missing values generated)

. replace class_ISED_difflevel=1 if natdiff_vanhanen>=0  & subnatdiff_ised>=0
(39 real changes made)

. replace class_ISED_difflevel=2 if natdiff_vanhanen<0  & subnatdiff_ised>=0
(5 real changes made)

. replace class_ISED_difflevel=3 if natdiff_vanhanen<0  & subnatdiff_ised<0
(5 real changes made)

. replace class_ISED_difflevel=4 if natdiff_vanhanen>0  & subnatdiff_ised<0
(5 real changes made)

. replace class_ISED_difflevel=. if subnatdiff_ised==.
(22 real changes made, 22 to missing)

. *TABULATE 
. tab class_ISED_difflevel period, col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

class_ISED |              period
_difflevel |         1          2          3 |     Total
-----------+---------------------------------+----------
         1 |         3          7          7 |        17 
           |     37.50      58.33      58.33 |     53.12 
-----------+---------------------------------+----------
         2 |         2          1          2 |         5 
           |     25.00       8.33      16.67 |     15.62 
-----------+---------------------------------+----------
         3 |         2          1          2 |         5 
           |     25.00       8.33      16.67 |     15.62 
-----------+---------------------------------+----------
         4 |         1          3          1 |         5 
           |     12.50      25.00       8.33 |     15.62 
-----------+---------------------------------+----------
     Total |         8         12         12 |        32 
           |    100.00     100.00     100.00 |    100.00 

. *GENERATE QUADRIANT CLASSIFICATION FIDALGO
. gen class_FIDALGO_difflevel=. 
(54 missing values generated)

. replace class_FIDALGO_difflevel=1 if natddiff_vdempol>=0  & subnatdiff_fidalgo>=0
(25 real changes made)

. replace class_FIDALGO_difflevel=2 if natddiff_vdempol<0  & subnatdiff_fidalgo>=0
(10 real changes made)

. replace class_FIDALGO_difflevel=3 if natddiff_vdempol<0  & subnatdiff_fidalgo<0
(9 real changes made)

. replace class_FIDALGO_difflevel=4 if natddiff_vdempol>0  & subnatdiff_fidalgo<0
(9 real changes made)

. replace class_FIDALGO_difflevel=. if subnatdiff_fidalgo==.
(20 real changes made, 20 to missing)

. *TABULATE 
. tab class_FIDALGO_difflevel period, col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
| column percentage |
+-------------------+

class_FIDA |
LGO_diffle |              period
       vel |         1          2          3 |     Total
-----------+---------------------------------+----------
         1 |         4          1          1 |         6 
           |     36.36       8.33      10.00 |     18.18 
-----------+---------------------------------+----------
         2 |         1          2          6 |         9 
           |      9.09      16.67      60.00 |     27.27 
-----------+---------------------------------+----------
         3 |         1          5          3 |         9 
           |      9.09      41.67      30.00 |     27.27 
-----------+---------------------------------+----------
         4 |         5          4          0 |         9 
           |     45.45      33.33       0.00 |     27.27 
-----------+---------------------------------+----------
     Total |        11         12         10 |        33 
           |    100.00     100.00     100.00 |    100.00 

. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/javier/Desktop/Papers/1. Under Review/Subnational Decoupling/1. Perspectives Submission/Review Round_2/T
> EXT, TABLES AND FIGURES/0Replication Files/MRDLog.log
  log type:  text
 closed on:  22 Jun 2024, 19:12:45
----------------------------------------------------------------------------------------------------------------------------
